I am an extremely viable individual. When I was settling on a progression of articles planned to be both enlightening and reasonable, I understood that one of the missing variables in individuals being more solid than they as of now are is that they don't have a clue how to settle on legitimate decisions in their way of life. I have composed a progression of articles to help you in your mission toward accomplishing and keeping up ideal wellbeing. Better wellbeing tomorrow starts by understanding what changes to make in your way of life and after that creation better decisions in what you do and what you eat once a day. Where we are later on is controlled by the little changes we make every day. Applying these progressions reliably after some time will take you basically anyplace you might want to go in your wellbeing and in your future.
How sound you CAN be is dictated by numerous elements things like hereditary constraints, your history of youth infections, and numerous different things that are outside your ability to control. In any case, by a long shot, the Main reason that individuals are not in their best condition of wellbeing is that nobody has shown them how to settle on better way of life decisions. Things like smoking or not, regardless of whether you drink liquor, what you eat, how much rest you get each night, and that's just the beginning. I have been rehearsing in the field of wellbeing and social insurance for more than 30 years, and it never stops to flabbergast me how little the normal individual thinks about how the decisions they make influence their wellbeing and their future.
"You are today the result of the considerable number of decisions and choices that you have made in your life as yet." Does that sound good to you? On the off chance that that is valid, and it is, at that point where you are a major part of your life and wellbeing a long time from now will be controlled by the decisions that you start making beginning TODAY. It is NEVER past the point where it is possible to begin settling on better choices with respect to your wellbeing, and it WILL decide your future.
In these articles, I will traverse a wide assortment of subjects all intended to give reasonable strides toward accomplishing the best condition of wellbeing that you can accomplish. Despite your own constraints that are outside your ability to control, you CAN be more solid than you are today. Little changes in the decisions you set aside a few minutes will help you beyond what you can envision right now. In this article I will talk about the significance of activity and the job it plays on getting to be as sound as is conceivable. Keep in mind, you don't need to begin preparing for a long distance race by tomorrow, yet you should start MOVING. Our bodies were intended to move and be dynamic, and I will do my best to enable you to settle on better decisions concerning what you do amid the day, the exercises you partake in, and how to practice essentially and viably on your way toward better wellbeing.
Our condition of wellbeing is certifiably not a "static" thing. Actually, it is the polar opposite. It is a dynamic, continually changing, here and there slippery element that requires consistent consideration. Wellbeing and your personal satisfaction start with decisions that you make. On the off chance that you settle on awful decisions over some undefined time frame, you will pay dearly. Then again, great decisions in issues that influence your wellbeing can pivot what seems, by all accounts, to be a sad circumstance in a sensibly brief timeframe. A standout amongst the most essential decisions with respect to wellbeing is the consideration of some type of activity in your every day schedule. You should make this a need since research has demonstrated that activity consistently is considerably progressively essential to your wellbeing and your future than what you eat! The general crowded gives much more consideration to their eating regimen than they do whether they work out, so by applying this to your life, you will be in front of the majority of the general population you know with respect to your condition of wellbeing in a moderately brief timeframe.
I can hear some of you presently saying things like "I'm too old to even consider beginning an activity program", and "In the event that I don't have any vitality currently, by what means can I gather enough additional solidarity to work out". "I'm as of now worn out. Won't practicing simply make me much progressively worn out?" Different reasons incorporate "I don't have time", and "I can't stand to join a fitness center or an exercise center". Everything I can say because of these reasons is Surrender THEM! Everybody can practice successfully as ahead of schedule as 3 and 4 years of age, and nobody is excessively old or excessively flabby to start a program of improving your dimension of wellness.
You can be similarly as compelling as you continued looking for showing signs of improvement shape in your lounge, family room, or sanctum than you can in a fitness center, so you can't blame the absence of cash. You don't have to purchase a bundle of loads and extra gear, yet can utilize what is accessible in your very own home. By putting aside as meager as 15 minutes of the day, you can be headed to encountering the advantages related with reliable exercise. Everybody can discover 15 minutes out of every day-take off of bed 15 minutes sooner, don't hit the sack until you have finished your activities, utilize a segment of your lunch hour as opposed to talking, surrender some portion of one Network program or play out your activities while viewing your most loved show! The result far exceeds the "penance".
More vitality as opposed to depleting your current vitality, it helps with structure your stores of additional vitality.
Better flow in your arms, legs, feet and hands.
Expanded protection from disorder and infection.
Brought down cholesterol and a more grounded heart.
Improved processing and a higher rate of digestion which will enable you to consume increasingly fat!
Better muscle tone and more solidarity to run with it.
Look better and feel more youthful exercise is really a "wellspring of youth".
On the off chance that we recorded them all there wouldn't be room in this article for whatever else! God gave us a body in which to live amid our stay on this planet. Alongside it, He gave us the duty to take legitimate consideration of it. A noteworthy piece of this consideration of our bodies lies in the significance of practicing and keeping it fit and solid. This is the thing that empowers us to take the necessary steps he has called every one of us to in our day by day lives. The Good book says that individuals die for an absence of learning. Numbness of how the body functions and what makes it work getting it done is no reason when the data is promptly accessible. I heard an extraordinary explanation that essentially stated, "In the event that you destroy your body, where are you going to live?" We have a commitment both to ourselves and to God to pick up all that we can about the consideration of our natural vessels (our bodies), and afterward to apply what we realize consistently. With exercise, the consistency of your exertion ought to be once a day.
On the off chance that you will simply begin the procedure with 15 minutes out of each day and do this for a time of 30 days, it will end up being a propensity. You will see little changes at first, yet the longest voyage in your street toward recovering your wellbeing and wellness starts with a solitary advance! The publicizing essayists for Nike hit the nail on the head when they instituted the adage, "Get it done". The first and most critical single step is simply to begin! Nobody is so out of sight shape that there is nothing they can do as activities, so you can't ask off with that pardon either. When you start, you will see that you didn't have anything to fear and no satisfactory reason, so you never need pardons again.
Start by executing two unique kinds of activities into your day by day schedule. The first is a straightforward arrangement of extending works out. Twist around and reach toward your toes keeping a slight bend in your lower back. You will see I didn't state contact your toes, as it might have been some time since some of you even observed your toes! Simply reach forward until you feel a good stretch in your back and your leg muscles. Next, reach up and extend for the sky. Continuously make sure to inhale when you are extending. I don't need anybody to go out amid this procedure! I realize that sounds senseless, yet you'd be astonished at how regularly we hold our breath as opposed to recharging our bodies with a crisp supply of oxygen. Truth be told, profound breathing is another type of activity, so you've just started to get some activity each time you slowly inhale. It wasn't so difficult all things considered! Rehash every one of those stretches multiple times and hold each time you extend for 5 seconds.
For those of you who are kept to a seat or the like, or for those of you that are awkward representing any period of time, play out a similar two stretches with your profound breathing while in a sitting position. This won't keep you from twisting forward or from coming to toward the sky.
The second sort of activity to start is a light type of reinforcing. For instance, hold a container of soup or a jug of water with your arms next to you. Curve your elbows and raise the can or bottle toward your shoulder, flexing the arm. At that point reach over your head with the equivalent can as the second exercise development. This will reinforce both your arms and your shoulders. Rehash every one of these multiple times and as it gets to simple, include a heavier can or a hand held load or some likeness thereof and increment to 2 sets of 10 of both of these developments. Once more, these should be possible both standing and sitting. Moreover, in the event that you are standing, grab hold of a seat and curve at the knees going down just to the extent is agreeable in the first place.
As you get more grounded and progressively OK with the development, attempt to hunch down most or the majority of the path before returning to a standing position. Utilize the seat to help you in keeping up your equalization and supporting you in coming up until your legs are sufficient not to require the assistance. In the event that you are sitting, take a stab at standing up from your seat multiple times and after that gradually sitting down into your seat. Sufficiently straightforward, yet this is a powerful strategy for giving solidarity to the legs and lower body and the bigger muscle gatherings.


text:
unsigned char ADC_Value;
unsigned char sharp, distance, adc_reading;
unsigned int value;
float BATT_Voltage, BATT_V;

void adc_pin_config (void)
{
 DDRF = 0x00; //set PORTF direction as input
 PORTF = 0x00; //set PORTF pins floating
 DDRK = 0x00; //set PORTK direction as input
 PORTK = 0x00; //set PORTK pins floating
}

//This Function accepts the Channel Number and returns the corresponding Analog Value
unsigned char ADC_Conversion(unsigned char Ch)
{
	unsigned char a;
	if(Ch>7)
	{
		ADCSRB = 0x08;
	}
	Ch = Ch & 0x07;
	ADMUX= 0x20| Ch;
	ADCSRA = ADCSRA | 0x40;		//Set start conversion bit
	while((ADCSRA&0x10)==0);	//Wait for ADC conversion to complete
	a=ADCH;
	ADCSRA = ADCSRA|0x10; //clear ADIF (ADC Interrupt Flag) by writing 1 to it
	ADCSRB = 0x00;
	return a;
}
unsigned int Sharp_GP2D12_estimation(unsigned char adc_reading)
{
	float distance;
	unsigned int distanceInt;
	distance = (int)(10.00*(2799.6*(1.00/(pow(adc_reading,1.1546)))));
	distanceInt = (int)distance;
	if(distanceInt>800)
	{
		distanceInt=800;
	}
	return distanceInt;
}
//Function to Initialize ADC
void adc_init()
{
	ADCSRA = 0x00;
	ADCSRB = 0x00;		//MUX5 = 0
	ADMUX = 0x20;		//Vref=5V external --- ADLAR=1 --- MUX4:0 = 0000
	ACSR = 0x80;
	ADCSRA = 0x86;		//ADEN=1 --- ADIE=1 --- ADPS2:0 = 1 1 0
}

redpajama_set_name: RedPajamaGithub


text:
New Jersey Arrests
Find arrest records in New Jersey
New Jersey Criminal Procedure
New Jersey Arrest Records and Warrant Search
Please fill in the form below to begin your New Jersey criminal records search
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By searching you certify that you are above 18 years of age
The criminal procedure of New Jersey is not very different from that of other states; the basic steps used for processing adult criminals are almost the same across the country. However, understanding the legal intricacies that surround arrests, the issue of active warrants from New Jersey and the eventual legal procedure used to try the accused can help you to effectively wade through the judicial system of the state.
Of arrest warrants and detentions
A police officer can make arrests without NJ outstanding warrants if he/she has reason to believe that a felony has been committed and that there is probable cause to believe that a certain individual was responsible for commissioning this criminal act. However, if the criminal incident in question is a misdemeanor, the officer has to be a witness to the crime to detain the accused without an active warrant.
When arrest warrants are required, these are sought from the local courts by filing an affidavit which ought to bring out probable cause clearly. Regardless of whether arrests are made with or without warrants, the detainee is covered by the Miranda rights. Pursuant to this, the accused is supposed to be warned that his actions and words will be held against him in the court of law hence as such the police cannot use pressure tactics to force the suspect into confessing.
Booked and taken to court
The booking takes place after the arrest; the accused is taken to the local police station where he is fingerprinted and photographed for records. All personal belongings are confiscated including any form of personal identification being carried by the accused which will be used to positively identify him. In NJ, it is the norm to also conduct a medical screening at this point.
The first appearance of the detainee in court is usually held within 24 hours of being arrested and this session is often coupled with the arraignment. During the latter, the rights of the defendant are explained to him along with the charges being filed against him. Bail petition will also be considered at this time.
Plea bargains and grand jury indictment
The majority of the criminal cases are sorted through plea bargains which help to save the state's judicial resources. A plea arrangement is a compromise between the defense and the prosecution under which the defendant agrees to plead guilty and the prosecution does its bit by reducing the charges. If a consensus cannot be reached between the two sides, the matter goes to trial.
A grand jury indictment may be held before things get to the stage of the plea bargain or even before the case goes to trial. This is a session in which a jury takes the call on whether the evidence is enough to support the charges framed against the accused. If the proof is sufficient, the jurors return the indictment and an arrest warrant is issued for the detention of the accused if he is not already being held in custody.
The trial and verdict
The defense can choose whether the case will be heard by the jury or the judge. If the matter involves jurors, the judge will act as the controlling authority in the courtroom but he will not actually participate in deciding on the guilt of the accused. In NJ, all defendants are to be brought to trial within 180 days.
Through these three months, all the evidence is presented before the court and it is carefully dissected by the opposing team. The closing arguments are made at the end of the trial at which point the jury members are sent into a room to decide on the case. Their decision has to be unanimous for the verdict to be valid. If the jurors find the defendant guilty, the judge sentences the accused.
New Jersey Outstanding Warrant Search
New Jersey Crime Information Center
New Jersey Court System
New Jersey Active Warrant Search
ACCEPTABLE USE POLICY AND TERMS
Important Note: This website is not affiliated with the U.S. Government or any State or Federal government agency and is not an official source of information. This is a private site. For any questions you may have please consult a lawyer.
Information found on this site must be used for lawfully acceptable purposes only. It is against the law to stalk or harass anyone using the information found on this site. Visitors cannot search for public officials, minors, or celebrities. Anyone who uses this site to access information through purposeful misrepresentation of themselves, or uses the information accessed in illegal pursuits may face criminal and civil charges. Information that can be found on this site is compiled from records that may be incomplete or have errors. The owners of this site do not own the records found on this site or any public records database. All records presented on this site are gathered from third party databases that are not controlled by the owners of this site. Every search is subject to applicable laws, to this site's terms of use and to the terms of use of the third party vendors that provide access to such information. The website owners receive compensation if you complete a registration through our website. When you complete such registration you will receive access to a third party membership area - of which the owners of this website have no control of. Please further read this website's entire terms of use.
Copyright ©2011- njarrests.org. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


text:
A jab in time Some Western countries have lower vaccination rates than poor parts of Africa. Anti-vaxxers are not the main culprits
International Vaccination
ERADICATING a disease is the sort of aim that rich countries come up with, and poor ones struggle to reach. But for some diseases, the pattern is reversed. These are the ailments for which vaccinations exist. Many poor countries run highly effective vaccination programmes. But as memories of the toll from infectious diseases fades across the rich world, in some places they are making a comeback.
The World Health Organisation (WHO) reckons that vaccines save 2.5m lives a year. Smallpox was eradicated in 1980 with the help of a vaccine; polio should soon follow. In both cases, rich countries led the way. The new pattern looks very different.
The trend is most evident for measles, which is highly contagious. At least 95% of people must be vaccinated to stop its spread (a threshold known as "herd immunity"). Although usually mild, it can lead to pneumonia and cause brain damage or blindness. The countries with the lowest vaccination rates are all very poor, but many developing countries run excellent programmes (see chart). Eritrea, Rwanda and Sri Lanka manage to vaccinate nearly everyone. By contrast several rich countries, including America, Britain, France and Italy, are below herd immunity.
Last year Europe missed the deadline it had set itself in 2010 to eradicate measles, and had almost 4,000 cases. America was declared measles-free in 2000; in 2014 it had hundreds of cases across 27 states and last year saw its first death from the disease in more than a decade. The trends for other vaccine-preventable diseases, such as rubella, which can cause congenital disabilities if a pregnant woman catches it, are alarming, too.
This sorry state of affairs is often blamed on hardline "anti-vaxxers", parents who refuse all vaccines for their children. They are a motley lot. The Amish in America spurn modern medicine, along with almost everything else invented since the 17th century. Some vegans object to the use of animal-derived products in vaccines' manufacture. The Protestant Dutch Reformed Church thinks vaccines thwart divine will. Anthroposophy, founded in the 19th century by Rudolf Steiner, an Austrian mystic-cum-philosopher, preaches that diseases strengthen children's physical and mental development.
INTERACTIVE: Explore vaccination coverage and measles cases worldwide from 1980 to 2014
In most countries such refuseniks are only 2-3% of parents. But because they tend to live in clusters, they can be the source of outbreaks. A bigger problem, though, is the growing number of parents who delay vaccination, or pick and choose jabs. Studies from America, Australia and Europe suggest that about a quarter of parents fall into this group, generally because they think that the standard vaccination schedule, which protects against around a dozen diseases, "overloads" children's immune systems, or that particular vaccines are unsafe. Some believe vaccines interfere with "natural immunity". Many were shaken by a claim, later debunked, that there was a link between autism and the MMR vaccine, which protects against measles, mumps and rubella.
In America, some poor children miss out on vaccines despite a federal programme to provide the jabs free, since they have no regular relationship with a family doctor. Some outbreaks in eastern Europe have started in communities of Roma (gypsies). Members of this poor and ostracised minority are shunned by health workers and often go unvaccinated.
Several governments are trying to raise vaccination rates by making life harder for parents who do not vaccinate their children. A measles outbreak last year that started with an unvaccinated child visiting Disneyland and spread from there to seven states prompted California to make a full vaccination record a condition of entry to state schools. The previous year, in a quarter of schools too few children had been vaccinated against measles to confer herd immunity. A dozen other states are considering similar bills. After a toddler died from measles last year, Germany recently started to oblige parents who do not wish their children to be vaccinated to discuss the decision with a doctor before they can enroll a child in nursery. Australia's new "no jabs, no pay" law withdraws child benefits from parents who do not vaccinate, unless they have sound medical reasons.
Persuasion, a fine art
There is, however, surprisingly little evidence that tough laws make a big difference to vaccination rates. European countries that are similar in most respects (such as the Nordics) may have similar rates for jabs that are mandatory in one country but not in another—or very different rates despite having the same rules. Rates in some American states where parents can easily opt out are as high as in West Virginia and Mississippi, which have long allowed only medical exemptions.
And strict rules may even harden anti-vaccination attitudes. Australia had previously made exemption conditional on speaking to a doctor or nurse about the benefits of vaccines. The new rules mean fewer chances to change parents' minds. Research suggests that making it harder to avoid the most important vaccines may make it more likely that people who strongly oppose vaccination in general shun optional ones, says Cornelia Betsch of the University of Erfurt.
More important, say public-health experts, is to boost confidence in the safety of vaccines and trust in the authorities that recommend them—both badly damaged in many European countries by pastpublic-health mis-steps, such as a scandal with contaminated blood supply in France from the late 1990s. The best way to handle a vaccine scare is to express empathy and promptly share the results from investigations of alleged adverse reactions, says Heidi Larson of the London School of Hygiene and Tropical Medicine. British authorities' dismissive response to the MMR scare failed to reassure worried parents.
One promising new approach is to keep track of the vaccine myths circulating in cyberspace and rebut each one as it appears. This requires tracking information from search engines and following anti-vaccination websites and parents' forums. On one such forum, worriers say they have scoured government and vaccine-manufacturer websites but feel overwhelmed by information that they regard as inconclusive or contradictory. One mother seeks advice on how to get around California's "fascist" new rule. Another casts doubt on a study on severe allergic reactions to vaccines: 33 cases from 25m jabs, she says, seems "fishily low".
Some countries are starting information campaigns that treat such concerns with respect. A parents' organisation in Bulgaria launched one recently, under the auspices of the ministry of health and the national association of paediatricians. Its website is jargon-free and easier to navigate than unwieldy official hubs. France is launching a national dialogue on vaccines this spring, with a website where citizens can swap gripes, worries and advice.
Although vaccine-hesitant parents often search for answers on the internet, their most trusted sources are doctors and nurses. The WHO recently developed guidelines to help health workers figure out, through a questionnaire, which type of worrier a parent is—and how to alleviate specific concerns. But recent research from several European countries shows that many doctors and nurses are also hesitant about vaccines, for much the same reasons as their patients. In a survey conducted in 2014, 16-43% of French family doctors said they never or only sometimes recommended some of the standard vaccines.
An additional problem is that many adults were not immunised as children and have not caught up since. In the 1970s and 1980s, when the measles vaccine was new, many children did not receive it, or got just one shot, which is now known not to be reliable in conferring immunity. Some countries offer free catch-up jabs to some adults when outbreaks flare up—usually parents with small children and health workers in affected areas.
But such efforts have, on the whole, been too little, too late. The return of easily preventable diseases that had all but disappeared is a shame. A bigger shame would be for governments to continue blaming it all on ignorant parents.
http://www.economist.com/news/international/21695387-some-western-countries-have-lower-vaccination-rates-poor-parts-africa-anti-vaxxers
Labels: Anti-Vaccination


After a morning lecture introducing a range of our projects to the 11 participants of Curating Lab 2014, we proposed a challenging exercise: to define a succinct "about us" paragraph for three imaginary institutions. (Named after the first three Pacific hurricanes of the 2014 season, no less.) Following the root of word "institution" (to establish, to set up), the task was not an architectural challenge nor a branding exercise, but one that first asked what practices are imagined to be initiated, how programmes are established and for whom are they set up.
Participants took into account the parameters we described for each of three art-institution typologies – a residency/studio programme; an annual festival and acommissioning body. After a one hour brainstorm within each group, participants came up with a paragraph that concisely communicated the "vision and values" of the new entities, responding to "what we do, how do we do it and why we do it that way".
The task wasn't oriented towards building a "business plan", though an integral part involved consideration of funding models, and who or what has a stake in the imagined organisation – city/state, philanthropists/donors, users/members/tickets, prizes/open calls, etc. We didn't expect job descriptions, but participants were encouraged to consider what kind of roles the staff within the imaginary institution might take up – would it have a core full-time team or occasional collaborators? What skills or roles would be taken up across curating, producing, publishing, researching, hosting, leadership, and so on?
The exercise was not to come up with a pitch to potential supporters (i.e. in future tense), but to exercise imagination in the present and to define what an organisation stands for and actually does in a succinct way. The aim was to think holistically about their "pet" organisation, its different programme strands and its relation to its local/international network.
1. CRISTINA – A residency/studio programme.
"Cristina provides residential, and studio facilities for national and international artists. It is based in several live/work spaces and as yet undefined rooms in a former school building in a small town on a remote island in an area of outstanding natural beauty. It is a day's travel to the nearest city. The local community of 800 people are mostly involved in fishing and farming."
2. AMANDA – An annual festival.
"Amanda is an event-based format and is based in a major densely-populated prosperous city with a strong corporate and media culture as well as several world-renowned museums, contemporary art centres and universities. The events take place in a specific block of time and involve organised, extended presentations and places for viewing them."
3. BORIS – A commissioning body.
"Boris invites artists to make temporary artworks in public spaces and locations that are not galleries or museums. It is based in a provincial port city that used to have a thriving economy based on heavy industry. The city is suffering from high unemployment, there are many abandoned buildings and wastelands."
"Latitudes in conversation with Yasmil Raymond"
Free admission. Limited seating. With simultaneous translation.
This event is part of Latitudes' ongoing #OpenCurating research, which analyses the implications of Web 2.0, participation and transparency for contemporary art production and programming. The core of #OpenCurating is formed through a series of interviews, freely available online, most recently with Steven ten Thije (Van Abbemuseum, Eindhoven), Sònia López and Anna Ramos (MACBA, Barcelona), and Badlands Unlimited (New York).
The conversation with Dia Art Foundation's curator Yasmil Raymond will address Dia's historical identity, the evolving role of the curator, and Raymond's vision in commissioning and preserving art projects. The dialogue will be later transcribed and published as the seventh interview of the #OpenCurating research series.
The evening will incorporate "crowd-sourced" questions by the public previously solicited via Twitter (hashtag #OpenCurating) and Facebook (http://www.facebook.com/LTTDS).
Yasmil Raymond has been Curator at the Dia Art Foundation since 2009, where she has organized exhibitions and projects with artists such as Jean-Luc Moulène (2012); Yvonne Rainer (2011-12); Koo Jeong A (2010-11); Franz Erhard Walther (2010-2012); and Trisha Brown (2009-10). Prior to joining Dia, Raymond worked at the Walker Art Center (2004–2009) in Minneapolis where she organized solo exhibitions with Tomás Saraceno (2009), Tino Sehgal (2007) and group exhibitions including Abstract Resistance (2010); Brave New Worlds (2007, co-curated with Doryun Chong). Raymond studied at The School of the Art Institute of Chicago (1999) and the Center for Curatorial Studies, Bard College (2004).
As a non-profit institution founded in 1974, the Dia Art Foundation is renowned for initiating, supporting, presenting, and preserving art projects. Between 1987 and 2004, the Dia Center for the Arts in Chelsea, New York, saw presented site-specific exhibitions and projects including those by Robert Gober, Jenny Holzer, Jorge Pardo and Pierre Huyghe. Dia:Beacon opened in 2003 in upstate New York, as the home for Dia's distinguished collection of art from the 1960s to the present. Dia Art Foundation maintains long-term, site-specific projects including Walter De Maria's The New York Earth Room (1977) and The Broken Kilometer (1979), Max Neuhaus's Times Square (1977), Joseph Beuys's 7000 Eichen (7000 Oaks) (1988), and Dan Flavin's untitled (1996), all in Manhattan; the Dan Flavin Art Institute in Bridgehampton, New York; De Maria's The Vertical Earth Kilometer (1977) in Kassel, Germany; Robert Smithson's Spiral Jetty (1970) in the Great Salt Lake, Utah; and De Maria's The Lightning Field (1977) in Quemado, New Mexico. Currently Dia is developing a project space on West 22nd Street in New York City.
#OpenCurating is a research project by Latitudes produced through BCN Producció 2012. La Capella, Barcelona City Council.
"Latitudes conversa con Yasmil Raymond"
Auditori MACBA, BarcelonaEntrada gratuita. Aforo limitado. Con traducción simultánea.
Este evento es parte del proyecto de investigación #OpenCurating de Latitudes, enfocado en el análisis de las implicaciones de la web 2.0, así como la expectación de participación y transparencia, en la producción y programación de arte contemporáneo. El núcleo de #OpenCurating consiste en una serie de diez entrevistas, disponibles en línea y gratuitas, con artistas, comisarios y escritores tales como Steven ten Thije (Van Abbemuseum, Eindhoven), Sònia López and Anna Ramos (MACBA, Barcelona), y Badlands Unlimited (Nueva York).
La conversación con la comisaria del Dia Art Foundation, Yasmil Raymond se centrará en la identidad histórica del Dia, la evolución del rol del comisario y la visión de Raymond en el encargo y el comisariado de proyectos artísticos. La charla será posteriormente transcrita y se publicará como la séptima entrevista en la serie #OpenCurating .
La sesión incorporará preguntas previamente enviadas por el público a través de Twitter (@LTTDS con hashtag #OpenCurating) y Facebook (http://www.facebook.com/LTTDS).
Yasmil Raymond ha sido Comisaria del Dia Art Foundation en Nueva York desde el 2009, donde ha organizado exposiciones y proyectos de artistas como Jean-Luc Moulène (2012); Yvonne Rainer (2011–12); Ian Wilson (2011–13); Robert Whitman (2011); Koo Jeong A (2010-11) (2010-11); Franz Erhard Walther (2010-2012); y Trisha Brown (2009–10). Anteriormente Raymond trabajó en el Walker Art Center en Minneapolis, donde organizó exposiciones individuals de Tomás Saraceno (2009), Tino Sehgal (2007) y exposiciones colectivas como Abstract Resistance (2010); Statements: Beuys, Flavin, Judd (2008); y Brave New Worlds (2007, co-comisariada con Doryun Chong). Raymond estudió en The School of the Art Institute of Chicago (1999) y cursó un máster en el Center for Curatorial Studies, Bard College (2004).
Como institución sin ánimo de lucro fundada en el año 1974, el Dia Art Foundation es conocida por haber iniciado, apoyado, presentado y preservado proyectos artísticos. Entre 1987 y 2004, el Dia Center for the Arts en Chelsea, Nueva York, presentó exposiciones y proyectos site-specific de Robert Gober, Jenny Holzer, Jorge Pardo y Pierre Huyghe, entre otros. Dia: Beacon abrió sus puertas en 2003 en el norte del estado de Nueva York, como sede de la distinguida colección de arte desde la década de 1960 hasta la actualidad. Dia Art Foundation mantiene proyectos a largo plazo en sitios específicos tales como The New York Earth Room (1977) y The Broken Kilometer (1979) ambos de Walter De Maria; Times Square (1977) de Max Neuhaus, 7000 Eichen (7000 Oaks) (1988) de Joseph Beuys; untitled (1996) de Dan Flavin, todos en Manhattan; el Dan Flavin Art Institute in Bridgehampton, Nueva York; The Vertical Earth Kilometer (1977) de Walter De Maria en Kassel, Alemania; Spiral Jetty (1970) de Robert Smithson en Great Salt Lake, Utah; y The Lightning Field (1977) Walter De Maria en Quemado, Nuevo Mexico. En la actualidad, Dia está desarrollando un espacio para proyectos en la calle West 22nd de Nueva York.
#OpenCurating es un proyecto de investigación de Latitudes producido por La Capella. BCN Producció 2012 del Institut de Cultura de Barcelona.
All photos: Latitudes | www.lttds.org (except when noted otherwise in individual photo captions).


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Gay marriage petitions from Ohio, Kentucky, Michigan and Tennessee will now be heard by the U.S. Supreme Court. On Jan. 16, the Supreme Court announced that it planned to make a ruling about whether or not gay couples have a constitutional right to get married. The Supreme Court's decision on the issue will determine whether statewide gay marriage bans will be allowed to continue.
The Supreme Court will hear oral arguments about the constitutionality of gay marriage this April. By this June, the nine justices are expected to deliver their verdict. So far, gay marriage is legal in 36 states and the District of Columbia. Other states, including Ohio, have enacted gay marriage bans that were recently upheld in the U.S. Sixth Circuit Court of Appeals. If the bans are found to be unconstitutional by the Supreme Court, same-sex custody litigation in Ohio could change drastically.
The Supreme Court made its first ruling about gay marriage on June 26, 2013, when it rejected portions of the 1996 Defense of Marriage Act. In a 5-4 decision, the Supreme Court ruled that gay couples that had been legally married could receive the same federal benefits that are granted to heterosexual married couples. Although the ruling was considered a victory for advocates of gay rights, the decision did not go so far as to make gay marriage legal in every state.
Because Ohio does not yet recognize gay marriage, family law issues involving same-sex couples can be especially complicated. Instead of being heard in family court, same-sex custody matters are heard by a judge in juvenile court. For this reason, a person who is involved in a same-sex custody dispute in Ohio might want to speak with an attorney who is familiar with same-sex custody litigation.
Source: CNN, "Supreme Court agrees to take on same-sex marriage issue," Mary Kay Mallonee, Jan. 16, 2015
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text:
\section{Introduction}
\label{sec:intro}

Images taken underwater have a blue or green hue due to the fact that the red wavelength gets absorbed in the water easily. Also, underwater images suffer from degradation due to the scattering of light by particles present in the water. Due to the aforementioned reasons, the visibility of the scene is quite less.
Underwater color restoration aims to reconstruct the true color from images taken in underwater setting. This is essential for downstream tasks such as object recognition and semantic segmentation which have potential applications in monitoring sea life and marine research. Traditional methods~\cite{gdcp,blurriness,minimum_info} focus on inverting the underwater image formation model by considering different underwater and natural image priors. These priors fail to capture the complex underwater image formation model.

Another alternative to restore color are learning based methods ~\cite{Uplavikar_2019_CVPR_Workshops,UGAN,han2021cwr,Ucolor,unsupervised}. In recent years, Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs)  have shown great promise in a variety of low level computer vision tasks.
The major challenge in color restoration of underwater images using learning based methods which are generally supervised is the absence of real-world datasets which contain the ground truth. Finding the underwater and above water versions of the same scene is extremely difficult. The simplified underwater image formation model used by existing learning based methods such as  \cite{li2019underwater,Uplavikar_2019_CVPR_Workshops,Ucolor} to create synthetic paired data is given by

\begin{equation}
I_{c}(x)=J_{c}(x) t_{c}(x)+A_{c}\left(1-t_{c}(x)\right)
\end{equation}

where $J_{c}$ is the clean image, $I_{c}$ is the generated synthetic underwater image, $t_{c}$ is transmission map which reflects the fraction of the light reaching the camera after reflecting from point $x$ in the scene, $A_{c}$ is the ambient light in the scene. $t_{c}$ can be expressed by:
\begin{equation}
t_{c}(x)=e^{-\beta_{c} d(x)}
\end{equation}
where $\beta_{c}$ is the wide-band attenuation coefficient and d(x) is the distance between the camera and the scene.
This model is similar to the haze formation model, except that the underwater model considers a different wide-band  attenuation  coefficient for each  color channel. The performances of data driven learning-based methods is highly dependent on the quality of the training data used by them. Models trained on synthetic datasets created using this equation do not perform well when tested on real underwater datasets. This can be attributed to the fact that synthetic underwater datasets fails to capture the wide array of underwater conditions present in real life underwater conditions.

Recently  Akkaynak \textit{et al}.~\cite{akkaynak2019sea}  showed  that  using  this  variant of haze formation model for creating synthetic underwater data introduces significant errors and formulated a revised underwater image formation model~\cite{akkaynak2018revised}. The revised model is dependant on reflectance, object range, beam attenuation coefficient which are hard to model and hence it is difficult to generate synthetic data using it.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=\textwidth]{network_icivgip_final.pdf}
    \caption{Overview of our proposed approach. \textbf{Dataset Generation}: First, we generate synthetic underwater image from aerial RGBD data using UWCNN \cite{li2019underwater}. Next, we extract content code from the synthetic image. To get diverse underwater images, we extract style code from multiple underwater images. Using content code and multiple style codes, our framework generates multiple realistic underwater images using multimodal domain adaptation \cite{huang2018multimodal}. \textbf{Color Restoration}: We use a residual U-net \cite{UNet} architecture with channel attention blocks for the restoration task.}
    \label{Network}
\end{figure*}

We attempt to address these challenges by using a multimodal unsupervised image-to-image translation framework ~\cite{huang2018multimodal}.
To reduce the discrepancy between domains, our method first employs the
multimodal image translation network to translate images
from one domain to another.  To address the challenge of underwater image
distribution diversity, given a single image in the synthetic underwater domain,  our framework can produce many images corresponding to various styles of underwater images. Using this domain adaptation framework, we generate a more realistic underwater dataset. To the best of our knowledge, ours is the first method which applies multi-modal domain adaptation technique to generate a underwater image dataset which is not only diverse but is able to model real underwater conditions closely. To show the effectiveness of our novel underwater dataset, we train a simple but effective residual U-Net \cite{UNet} based architecture with channel attention blocks for the restoration task. Additionally, we observe that the performance of existing color restoration methods increases when trained our our domain adapted dataset.
In summary, the main contributions of our paper are:
\begin{itemize}
    \item We present a novel domain adapted underwater RGB dataset and the corresponding paired ground truth. This dataset covers various different water conditions for better generalizability.
    \item We use a simple and effective residual U-Net architecture with channel attention blocks, which is inspired by \cite{Zamir2021MPRNet}, for performing underwater restoration on our domain adapted dataset.
    \item We conduct extensive experiments on our domain adapted dataset and show that our proposed method performs better than existing state of the art methods on color restoration tasks.
\end{itemize}

\section{Related Work}

In this section, we briefly discuss the underwater color restoration methods which are related to our work. Depending upon the type of method used, underwater color restoration methods can be mainly categorized into either prior based methods, model-free methods or learning based methods.

\subsection{Prior based methods}

Image prior based color restoration methods attempt to estimate the parameters of underwater imaging models based on statistics of clear abovewater images. Some of the priors employed by these methods include general dark channel prior (DCP) ~\cite{gdcp} which applied a general variant of the DCP to the underwater restoration problem. To estimate the transmission map of underwater images, Galdran \textit{et al.} \cite{GALDRAN2015132} proposed a variant of the DCP based on red channel information. Based on a minimum information loss and histogram distribution prior, Li et al. \cite{hdcp}, proposed an color restoration algorithm. Peng \textit{et al.} ~\cite{blurriness} proposed a depth estimation method for underwater scenes based on image blurriness and light absorption. Scene depth can be used to restore the degraded underwater image. SeaThru \cite{akkaynak2019sea} is a physics based algorithm which takes RGBD as input and estimates the backscatter and the illumination of the scene using priors. It then uses the revised underwater image formation model \cite{akkaynak2018revised} to recover the true radiance of the scene. The priors assumed by these methods are not valid in all underwater scenarios due to which their performance is limited.

\subsection{Model-free methods}

Earlier attempts to restore color in underwater images involved adjusting the values of pixels to restore the color. They do not rely on any priors while restoring the image. Earlier solutions relied on histogram equalisation techniques \cite{histoeq} for restoration. Fusion based white balancing proposed by Ancuti \textit{et al.} ~\cite{8058463} proposed a two step strategy that involved white balancing and fusion to produce the color restored version. Fu \textit{et al.} \cite{7025927} proposed a retinex based approach for enhancing the underwater images.  To increase underwater image quality, Ghani et al. \cite{contrast} proposed a method to integrate global and local contrast stretching. Using these methods may result in distortion in colors and artifacts when illumination conditions are complex.

\subsection{Learning based methods}

There are several attempts to create a paired dataset which contains the underwater scene and the corresponding abovewater scene. Li \textit{et al}. ~\cite{li2019underwater} proposed to create a synthetic dataset using an underwater image formation model and created ten different types of underwater scenes based on the ten Jerlov water types. In~\cite{UGAN}, Fabbri \textit{et al}. proposed U-GAN approach which tries to translate an image from above water domain to underwater domain without predefined image pairs. It uses a CycleGAN based model to translate images from above water domain to under water domain. It then uses a CNN based model to restore images. Recently, Li \textit{et al}. ~\cite{li2019underwater} collected a paired underwater image dataset UIEBD using survey methods for training deep networks and proposed a gated fusion network to enhance underwater images. To collect this paired dataset, $12$ candidate ground-truth images for each underwater image were created using existing underwater image restoration methods. The authors used subjective comparisons among volunteers to select the best candidate image. Han \textit{et al}. \cite{han2021cwr} used physical apparatus and the camera response curve to measure the attenuation coefficients in water and used the old underwater image formation model to estimate the radiance of the scene. This dataset is limited in variety and has information about only one Jerlov water type.

Other underwater color restoration methods use architectures and loss functions which are tailored to underwater domain. Upvaikar \textit{et al}. \cite{Uplavikar_2019_CVPR_Workshops} used a nuisance classifier to classify the type of underwater type in addition to a CNN based encoder-decoder architecture to restore images. Sharma \textit{et al.} \cite{sharma2021wavelength} used an attentive skip mechanism to refine multi scale features.  Li et al. \cite{Ucolor} used an attention mechanism to integrate features from different color spaces to produce a restored version of the underwater image. However, due to the domain gap between synthetic and real data, these methods which are trained on synthetic underwater images do not perform well when tested with real underwater images.

\begin{figure*}[!t]
    \includegraphics[width=\textwidth]{Images/diverse_styles.jpg}
    \caption{For each RGB-D input, we generate various UW images using the style of reference real UW images.}
    \label{Dataset}
\end{figure*}

\section{Proposed method}

In this section, we describe the details of our multimodal domain adaptation framework for underwater color restoration. First, we provide details of the multimodal image translation module which is used to create a more realistic dataset. Then we describe restoration module which is used to obtain the restored image. Finally, we give the loss functions that are applied to train the proposed networks. An overview of our proposed approach is shown in Fig. \ref{Network}.

\subsection{Multimodal Domain Adaptation}

Due to the domain gap, underwater restoration models trained on synthetic datasets are ineffective on real images. Domain adaptation techniques  helps learning based methods which are trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. We perform domain adaptation between synthetic underwater images and a real underwater images to create our domain adapted dataset. Directly learning an image to image translation framework between real underwater images and above water images is not possible due to the huge gap between the two domains. Such a setup is susceptible to learning spurious correlations and does not yield good results.

Given a set of synthetic underwater images $I_{S}= \{I_{i}\}_{i=1}^{N_{suw}}$ and a set of real underwater images $I_{R}=\{I_{j}\}_{j=1}^{N_{ruw}}$ where $N_{suw}$ and ${N_{ruw}}$ denote the number of images in the synthetic underwater and the real underwater domain respectively, we aim to learn a domain adaptation model that can produce a diverse set of underwater images that resembles the real underwater conditions.

We assume that an image can be decomposed into a content code which is domain invariant and a style code which captures the properties specific to a domain.  Let $E_{S}$ and $E_{R}$ be the encoders of the synthetic underwater and real underwater domain respectively. An encoder decomposes an image into a content code $c$ and a style code $s$.  Let $G_{S}$ and $G_{R}$ be the decoders of the synthetic underwater and real underwater domain respectively. Given a content code $c$ and a style code $s$, the decoder $G$ outputs the corresponding image. The decoder uses adaptive instance normalisation layers (AdaIN) \cite{huang2017adain} to combine the content and the style and generate an image. To ensure that the \textbf{synthetic underwater  $\rightarrow$ real underwater images} obtained upon translation are similar to the real underwater images and vice versa, we make use of discriminators $D_{R}$ and $D_{S}$ and use an adversarial setup to train our framework. The architecture of the encoder, generator and the discriminator follows that from MUNIT \cite{huang2018multimodal}.

\noindent
\textbf{Notation:} We use the following notation while defining our loss functions. Let $I_{suw} \in I_{S}$ and $I_{ruw} \in I_{R}$. To translate a synthetic underwater image $I_{suw}$ to the real underwater domain, we first generate the content code $c_{S}$ using $E_{S}$. We then generate the $s_{R}$ using $E_{R}$ of a real underwater image $I_{ruw}$. To translate the image, we use $G_{R}$ to recombine the content code $c_{S}$ and the style code $s_{R}$ to get $I_{S\rightarrow R}$.

\begin{align*}
\begin{split}
    c_{S},s_{S} = E_{S}(I_{suw}) \\
    c_{R},s_{R} = E_{R}(I_{ruw}) \\
    I_{suw\rightarrow ruw} = G_{R}(c_{S},s_{R}) \\
\end{split}
\end{align*}

\begin{center}
\end{center}

\begin{center}
\end{center}
\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{result_1.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{result_7.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{result_19.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{Comparison of our method with recent state of the art methods on the SeaThru \cite{akkaynak2019sea} dataset. Our method is able to handle underwater color attenuation well and can restore it properly.}
    \label{Comparisons}
\end{figure*}
\vspace{-0.5cm}
\subsubsection{Loss functions used for Multimodal domain adaptation}

To train our multimodal domain adaptation framework, we use the following loss functions:

\noindent
\textbf{Style and Content Reconstruction loss:} We make use of cyclic reconstruction losses for the latent codes to ensure that the encoder and decoder for each of the domains are inverses of one another. The loss functions for style and content reconstruction are:

\begin{align}
\begin{split}
        \mathcal{L}^{R}_{\text{content}} =  \| E_{R}^{c}(G_{R}(c_{S},s_{R})) - c_{S} \|_{1} \\
        \mathcal{L}^{R}_{\text{style}} =  \| E_{R}^{s}(G_{R}(c_{S},s_{R})) - s_{R} \|_{1}
\end{split}
\end{align}

\noindent
\textbf{Identity mapping loss for images:} Using identity mapping losses for images also constraints the encoder and decoder for each of the domains to be inverses of one another. The loss functions used for identity mapping loss are:

\begin{align}
\begin{split}
        \mathcal{L}^{R}_{\text{img}} =  \| G_{R}(E_{R}^{c}(I_{ruw}),E_{R}^{s}(I_{ruw}))  - I_{ruw} \|_{1}
\end{split}
\end{align}

\noindent
\textbf{Adversarial Loss:} The adversarial loss reduces the discrepancy between the distribution of the real underwater images and the translated synthetic underwater to real underwater images. This loss is essential to ensure that the real underwater images and the translated synthetic are indistinguishable.
\begin{align}
\begin{split}
        \mathcal{L}^{R}_{\text{adv}} = log(1-D_{R}(G_{R}(c_{S},s_{R})) + log(D_{R}(I_{ruw}))
\end{split}
\end{align}

\noindent
\textbf{Edge Loss:} While translating images from the synthetic to real domain, we want to preserve edges and the structure of the synthetic underwater image. To calculate the edge loss, we first apply laplacian of the gaussian to extract the edges and then enforce L1 loss between the translated image and the original image.

\begin{align}
\begin{split}
    \mathcal{L}^{R}_{\text{edge}} = \sqrt{(\nabla^2(\hat{I}_{suw})- \nabla^2(\hat{I}_{suw \rightarrow ruw}))^2+\varepsilon^2}
\end{split}
\end{align}

$\nabla^2$ denotes the Laplacian operator and $\hat{I}_{suw}$, $\hat{I}_{suw \rightarrow ruw}$ are the Gaussian smoothed form of ${I_{suw}}$, ${I_{suw \rightarrow ruw}}$ respectively . We set the value of $\varepsilon$ to $10^{-4}$ in our experiments.

\noindent
\textbf{SSIM Loss:} To enforce the translation module to use the input image as a reference while translating from synthetic to real, we calculate the SSIM score between the input image and the translated version of it.

\begin{align}
\begin{split}
    \mathcal{L}^{R}_{\text{SSIM}} = 1 - SSIM(I_{suw \rightarrow ruw},I_{suw})
\end{split}
\end{align}

\noindent
We define the other loss functions such as $\mathcal{L}^{S}_{\text{content}}$, $\mathcal{L}^{S}_{\text{style}}$, $\mathcal{L}^{S}_{\text{adv}}$, $\mathcal{L}^{S}_{\text{SSIM}}$,$\mathcal{L}^{S}_{\text{edge}}$ for the translated images of the real underwater domain to the synthetic underwater domain symmetrically.

\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{Results/12.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results/21.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results/25.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{Visual Comparisons on the real UIEBD \cite{li2019underwater} dataset}
    \label{comparisons_uiebd}
\end{figure*}

\noindent
\textbf{Overall Loss Function:} Thus, the overall loss function is:

\begin{align}
\begin{split}
 \min_{E_{R}, E_{S}, G_{R}, G_{S}} & \max_{D_{R}, D_{S}}  \mathcal{L}\left(E_{R}, E_{S}, G_{R}, G_{S}, D_{R}, D_{S}\right) = \\ & \lambda_{content} (\mathcal{L}^{S}_{\text{content}} + \mathcal{L}^{R}_{\text{content}}) + \lambda_{style} (\mathcal{L}^{S}_{\text{style}} + \mathcal{L}^{R}_{\text{style}}) \\ & +  \lambda_{img} (\mathcal{L}^{S}_{\text{img}} + \mathcal{L}^{R}_{\text{img}})  +  \lambda_{adv} (\mathcal{L}^{S}_{\text{adv}} + \mathcal{L}^{R}_{\text{adv}}) \\  &+ \lambda_{edge} (\mathcal{L}^{S}_{\text{edge}} +
\mathcal{L}^{R}_{\text{edge}}) +\lambda_{ssim} (\mathcal{L}^{S}_{\text{SSIM}} + \mathcal{L}^{R}_{\text{SSIM}})
\end{split}
\end{align}

\noindent

where $\lambda_{content}$, $\lambda_{style}$, $\lambda_{img}$,$\lambda_{adv}$,  $\lambda_{ssim}$ and $\lambda_{edge}$ control the relative importance between the losses. Using the above setup, we try to learn a mapping between synthetic underwater domain and real underwater domain.

\subsection{Restoration framework}

We use an encoder-decoder framework which is based on the U-Net architecture \cite{UNet}. To exploit the inter-channel relationship of features, our framework consists of channel attention blocks (CAB) \cite{10.1007/978-3-030-01234-2_1}. We use CABs at every scale as well as at the skip connections of the U-Net. These blocks are capable of extracting multi-scale contextualized features \cite{Zamir2021MPRNet} quite well. It has been observed that using transposed convolution operation in the decoder part introduces checkerboard artifacts in the restored image. Therefore,  to increase the spatial resolution of features in the decoder, we use bilinear upsampling followed by a convolution layer. Finally, instead of directly predicting the restored image, our proposed restoration framework predicts a residual image $I_R$ to which the degraded input image $I_X$ is added to obtain: $I_{Y}$ = $I_X$ + $I_R$. where $I_{G}$ denotes the ground truth image.

\subsubsection{Loss Functions used for Restoration}

We use a combination of Charbonnier's loss \cite{charbo}, perceptual loss \cite{Johnson2016Perceptual} and edge loss to optimise our restoration network. Charbonnier's loss is an extension of the standard L1 loss which adds robustness to the optimization process. Perceptual loss encourages the network to produce natural looking images. Edge loss helps in preserving the high frequency details of the image. Thus, an optimal combination of these losses helps in restoring underwater images.

\begin{multline}
    \emph{L} = \sqrt{(I_{G}- I_{Y})^2+\varepsilon^2} + \lambda_{1} L_{perceptual}(I_{G},I_{Y})+\\ \lambda_{2} \sqrt{(\nabla^2(\hat{I_{G}})- \nabla^2(\hat{I_{Y}}))^2+\varepsilon^2},
    \label{eq:loss1}
\end{multline}

where $I_{G}$ denotes the ground truth image, $I_{Y}$ denotes the output of our model,  $\nabla^2$ denotes the Laplacian operator and $\hat{I_{Y}}$, $\hat{I_{G}}$ are the Gaussian smoothed form of ${I_{Y}}$, ${I_{G}}$ respectively . We set the value of $\varepsilon$ to $10^{-4}$ in our experiments. Here $\lambda_{1}$ and $\lambda_{2}$ control the relative importance of the three losses used.

\begin{table}
    \centering
    \begin{tabular}{|c | c  |}
     \hline
     Datasets & \# of images \\ [0.5ex]
     \hline\hline
     SeaThru \cite{akkaynak2019sea} & 1157  \\
     \hline
     HICRD \cite{han2021cwr}  & 2003 \\
     \hline
     RUIE \cite{ruie}  & 2181 \\
     \hline
     UIEBD \cite{uieqb} & 890  \\
     \hline
     SQUID \cite{berman2020underwater}  & 57 \\
     \hline
     \hline
     Total & 6288 \\ [1ex]
     \hline
    \end{tabular}
    \caption{Details regarding the real underwater datasets used in our work. No single real-world dataset features a large number of diverse underwater conditions. Therefore, to increase diversity, we use many sources.}
    \label{dataset_images}
\end{table}

\begin{table}[!h]
  \centering
  \begin{tabular}{|c | c | c |}
   \hline
   Methods & UCIQE $\uparrow$ & UIQM $\uparrow$ \\ [0.5ex]
   \hline\hline
   Input & 0.4663 & 2.0196  \\
   \hline
   DWN & 0.4582 &  4.9045 \\
   \hline
   FUnIE-GAN & 0.503 & 4.8331  \\
   \hline
   WaterNet & 0.5331 & 5.079 \\
   \hline
   Ucolor & 0.5218 & 4.7468 \\
   \hline
   Ours(simple U-Net) & 0.5363 & 5.7806 \\
   \hline
   Ours(w/o CAL) & 0.5419 & 5.7851 \\
   \hline
   Ours & \textbf{0.5642} & \textbf{6.0271} \\ [1ex]
   \hline
  \end{tabular}
\caption{UnderWater quantitative metrics (higher is better) for various methods when tested on SeaThru dataset \cite{akkaynak2019sea}.}
\label{Quant}
\end{table}

\vspace{-1cm}
\section{Experimental Details}

In this section, we first present the details on how we create our domain adapted dataset. Secondly, we provide details regarding training of our framework. Then, we provide quantitative and qualitative evaluations of our methods on real underwater images. Finally, we perform ablation studies to verify the effectiveness of our approach.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=\textwidth]{Images/Comparison_our_dataset.pdf}
    \caption{We compare the performance of various methods when trained on our domain adapted dataset versus the original dataset that the methods were trained on.
trained on }
    \label{comparison_our_ds}
\end{figure*}

\subsection{Creating domain adapted dataset}

\begin{figure}[htp]
    \centering
    \includegraphics[width=\columnwidth]{Images/Ablation.pdf}
    \caption{We train our network on other synthetic datasets such as UWCNN \cite{li2019underwater} and EUVP \cite{UGAN}. Network trained on our domain adapted dataset produces better results.}
    \label{Ablation}
\end{figure}
To perform domain adaptation, we need synthetic underwater data and real underwater data. We use $1449$ RGBD images from NYU-V2 dataset \cite{Silberman:ECCV12} to create synthetic underwater images according to the procedure given by \cite{li2019underwater}. We use the Type I and Type-III variants in our synthetic data. To create the real underwater dataset, we collate an extensive set of images from many different sources which cover a diverse range of underwater conditions. This allows our model to generalise well to unseen water types. Further details, regarding the sources and the number of images can be found in Table \ref{dataset_images}. We train the multimodal domain adaptation framework for this \textbf{synthetic underwater dataset $\leftrightarrow$ real underwater dataset} without using any paired information.

To generate our domain adapted dataset, we extract the content code from a synthetic underwater image and extract the style code from a randomly sampled real underwater image and perform domain adaptation using them, see Fig. \ref{Dataset}. More examples from our domain adapted dataset can be found in the supplementary material. For each synthetic underwater image, we randomly sample $6$ real underwater images from multiple real underwater images and extract the style code for each of these $6$ images. Using these styles codes and the content codes extracted from the synthetic underwater dataset, we create our domain adapted dataset.  Therefore, in total we have $1449\times6=8694$ images in our domain adapted dataset and their corresponding ground truth. Using this domain adapted dataset and the corresponding ground truth, we train our restoration framework on it. We plan to make this domain adapted dataset available for academic purposes and for further research.

\subsection{Training details}

The experiments for multimodal domain adaptation and the restoration framework were performed on Nvidia RTX 2080Ti using PyTorch framework.

\subsubsection{Multimodal domain adaptation}

To train our framework, we use images of size $128x128$. In all experiments, we use a batch size of 4 and train the framework for $10^5$ steps until all the loss functions stabilise. The weight parameters $\lambda_{content}$, $\lambda_{style}$, $\lambda_{img}$, $\lambda_{adv}$, $\lambda_{ssim}$, $\lambda_{edge}$ are set to $1$, $1$, $1$, $5$, $3$ and $50$ respectively.

\subsubsection{Restoration}

For our restoration framework, we use patches of size $256 \times 256$.  Horizontal and vertical flips are randomly applied as forms of data augmentation. We train the restoration framework for $75$ epochs. The weight parameters $\lambda_{1}$ and $\lambda_{2}$ are set to 0.5 and 0.5 respectively. We use the Adam optimizer with an initial learning rate of a $ 3\times10^{-4}$ which is halved after every $10$ epochs.

\begin{table}
  \centering
  \begin{tabular}{|c | c | c |}
   \hline
   Methods & PSNR $\uparrow$ & SSIM $\uparrow$ \\ [0.5ex]
   \hline\hline
   Input & 16.91 & 0.761 \\
   \hline
   DWN & 21.52 &  0.801 \\
   \hline
   FUnIE-GAN & 18.86 & 0.782  \\
   \hline
   WaterNet & 19.24 & 0.832 \\
   \hline
   Ucolor & 21.63 & 0.838 \\
   \hline
   Ours & \textbf{21.83} & \textbf{0.844} \\ [1ex]
   \hline
  \end{tabular}
\caption{Full-reference quantitative metrics for various methods when tested on the UIEBD Dataset\cite{li2019underwater}}
\label{quant_full_reference}
\end{table}

\subsection{Comparison with state-of-the-art methods}

We compare our method with existing state of the art deep methods \cite{sharma2021wavelength,islam2020fast,li2019underwater,Ucolor}. We test our method on the SeaThru \cite{akkaynak2019sea} dataset and UIEBD \cite{li2019underwater} dataset. As reported in Fig. \ref{Comparisons}, our method is able to remove the greenish/bluish hue from underwater images in the SeaThru dataset. Since ground truth data is not available for the SeaThru dataset, non reference underwater image restoration metrics like Underwater Color Image Quality Evaluation Metric (UCIQE) \cite{yang2015underwater} and
Underwater Image Quality Measure (UIQM) \cite{panetta2015human} are calculated and reported in Table \ref{Quant}.

Visual comparisons on the UIEBD dataset are reported in Fig. \ref{comparisons_uiebd}. Since, ground truth data is available for UIEBD, full reference image restoration metrics like PSNR, SSIM \cite{1284395} are calculated and reported in Table \ref{quant_full_reference}. We can see that our method performs better than the existing state of the art methods and produces more natural looking images. More color restoration comparisons against other underwater restoration methods are included in the supplementary material.

\subsection{Training other methods on our domain adapted dataset}

Our main contribution lies in the fact that we create a multimodal domain adapted dataset that reflects the underwater conditions closely. To verify the effectiveness of our dataset, we train other deep learning based methods such as \cite{li2019underwater},\cite{sharma2021wavelength} on our dataset. We found that when trained on our domain adapted dataset, these methods perform better (both qualitatively and quantitatively) on real images,  than when trained on their own synthetic dataset. The qualitative results of this experiment can been seen in Fig.\ref{comparison_our_ds} and quantitative results in Table \ref{metrics_our_ds}, where we have calculated non-reference underwater image metrics on the real SeaThru \cite{akkaynak2019sea} dataset. This experiment also validates our claim that methods which are trained on synthetic underwater datasets do not perform well when tested on real underwater datasets. However, when we train these algorithms on our domain adapted dataset, then the performance of these algorithms increase.

\subsection{Ablation study}
To demonstrate the efficacy of our dataset, we trained our network with UWCNN~\cite{li2020underwater} synthetic dataset and with a GAN generated dataset EUVP ~\cite{islam2020fast}. Restoration results by our network trained on the UWCNN dataset is shown in second column of Fig. \ref{Ablation}. We can clearly see some green tinge in the output. EUVP dataset is a GAN based dataset which has 11435 paired examples. The restoration results by our method trained on this dataset is shown in the third column. The results are slightly better than that of UWCNN. This can be attributed to the fact that the EUVP dataset was created using a GAN and can model the underwater conditions better.  Overall, our method trained on our domain adapted dataset produces the best restoration results which is seen in the fourth column.

We also tried out different variations of our network; 1) Simple U-Net, where we removed the residual connection and the channel attention layer from the channel attention block (CAB contains two convolutional layers followed by channel attention), 2) w/o Channel Attention Layer (CAL). All our variations perform better than existing methods. The results are shown in Table \ref{Quant}. Additional ablation studies on the losses used for the domain adaptation can be found in the supplementary material.

\begin{table}
    \centering
    \begin{tabular}{|c | c | c |}
     \hline
     Methods & UCIQE $\uparrow$ & UIQM $\uparrow$ \\ [0.5ex]
     \hline\hline
     Input & 0.4663 & 2.0196  \\
     \hline
     DWN [12] & 0.4582 &  4.9045 \\
     \hline
     DWN (\textbf{Our Data})  & \textbf{0.5375} & \textbf{5.6910}  \\
     \hline
     WaterNet \cite{li2019underwater} & 0.5331 & 5.079 \\
     \hline
     WaterNet \cite{li2019underwater} (\textbf{Our Data}) & \textbf{0.5421} & \textbf{5.84} \\
     \hline
     Ours & \textbf{0.5642} & \textbf{6.0271} \\ [1ex]
     \hline
    \end{tabular}
    \caption{Underwater quantitative metrics (higher is better) for various methods when tested on real SeaThru dataset. \textbf{Our Data} indicates that
    the particular algorithm was trained on our domain adapted dataset}
    \label{metrics_our_ds}
\end{table}

\section{Conclusions}

In this paper, we create a diverse underwater image dataset which reflects the real underwater conditions closely. To create this dataset, we use a multimodal domain adaptation framework to translate an image from the synthetic underwater domain to the real underwater domain. We proposed a simple residual U-net based architecture with channel attention modules for color restoration. We show that our method produces state of the art results qualitatively and also quantitatively on real underwater datasets. By performing extensive experiments on our domain adapted dataset, we show that our method increases the performance of existing underwater restoration algorithms. In the future, we plan to use the multimodal domain adaptation framework to solve other image restoration tasks such as low light restoration, reflection removal where obtaining the ground truth is not possible.
\begin{acks}
We gratefully acknowledge funding from the Department of Science and Technology (DST/ICPS/IHDS/2018).
\end{acks}
\bibliographystyle{ACM-Reference-Format}

\section{More studies for our paper}
We provide some more examples of our domain adapted dataset that we train our restoration model on. We provide additional results on real world underwater images. Additionally, we perform an ablation study on the SSIM loss and Edge loss used in the domain adaptation framework.  Also, we show some failure cases for our model.

\subsection{Examples of our domain adapted dataset}
To create a more realistic underwater image, we combine the content code of a synthetic underwater image with the style code of a real underwater image. We provide some more examples for our domain adapted dataset in Fig. \ref{examples_dataset}

\subsection{Comparisons with state-of-the-art methods}

We compare our method with existing state of the art deep methods \cite{sharma2021wavelength,islam2020fast,li2019underwater,Ucolor} for underwater restoration. In Fig. \ref{Comparisons}, we test our method on real underwater images collected by us during a diving expedition. Additionally, we test our method on other real underwater images of the SeaThru \cite{akkaynak2019sea} dataset, UIEBD \cite{li2019underwater} dataset and the HICRD dataset \cite{han2021cwr}. As reported in Fig. \ref{Comparisons_2} and Fig. \ref{Comparisons_3}, our method is able to remove the greenish/bluish hue from these underwater images. Thus, our restoration model is able to generalise to a wide variety of underwater conditions.

\subsection{Ablation on loss functions}

We introduce additional losses such as SSIM loss and Edge loss to the domain adapted framework of \cite{huang2018multimodal}. We observed that the domain adapted dataset created using the  standard framework \cite{huang2018multimodal} had a lot of structural information missing and also had artifacts. To show the effectiveness of the losses introduced by us, we perform an ablation study. In this study, we consider the baseline results as the results obtained using just the standard losses of \cite{huang2018multimodal}. The 'Baseline+Edge' are the results obtained using the standard losses and using an additional edge loss (Eqn. 6). 'Ours' denotes the results obtained using the standard framework, edge loss (Eqn. 6) and SSIM Loss (Eqn. 7). The quantitative results of the ablation study can be found in Fig. \ref{ablations}.

\subsection{Some failure cases of our model}

Some images restored by our model have a reddish tinge. Underwater images generally have a bluish or greenish tinge due to significant attenuation of the red channel. Thus, the network needs to account for this by increasing the value of red channel. For some images, we hypothesize that our model tries to overcompensate this process  due to which some restored images have an overtly reddish tinge.
A few failure cases can be found in Fig. \ref{failure_cases}.

\begin{center}
\end{center}
\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Clean & Synthetic Underwater & After domain adaptation & Style Image \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{more_results/output001.jpg}
    \includegraphics[width=\textwidth]{more_results/output004.jpg}
    \includegraphics[width=\textwidth]{more_results/output012.jpg}
    \includegraphics[width=\textwidth]{more_results/output015.jpg}
    \includegraphics[width=\textwidth]{more_results/output018.jpg}
    \caption{Some examples from our domain adapted dataset}
    \label{examples_dataset}
\end{figure*}

\begin{figure*}[!b]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{Dwarka_Results/4.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Dwarka_Results/6.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Dwarka_Results/7.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{Visual comparisons on the dataset collected by us during an expedition}
    \label{Comparisons}
\end{figure*}

\begin{center}
\end{center}
\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{Results_Supp/51.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/52.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/17.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/60.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/44.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/34.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{Visual comparisons on real underwater images}
    \label{Comparisons_2}
\end{figure*}

\begin{center}
\end{center}
\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{Results_Supp/19.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/2.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/13.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/10.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/11.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
   Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{Results_Supp/15.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{More visual comparisons on real underwater images}
    \label{Comparisons_3}
\end{figure*}

\begin{center}
\end{center}
\begin{figure*}[!t]
    \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
  >{\centering\arraybackslash}X
   }
    Synthetic underwater & Style Image & Baseline & Baseline+Edge & Ours \\
    \end{tabularx}
    \centering
    \includegraphics[width=\textwidth]{ablation/output005.jpg}
    \includegraphics[width=\textwidth]{ablation/output018.jpg}
    \includegraphics[width=\textwidth]{ablation/output022.jpg}
    \caption{Results from our ablation study}
    \label{ablations}
\end{figure*}

\begin{center}
\end{center}
\begin{figure*}[!t]
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{failure/57.jpg}
        \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    Input & DWN~\cite{sharma2021wavelength}  & FUnIE-GAN~\cite{islam2020fast}  & WaterNet~\cite{li2019underwater} & Ucolor~\cite{Ucolor} & Ours \\
    \end{tabularx}
    \includegraphics[width=\textwidth]{failure/59.jpg}
            \begin{tabularx}{\textwidth }{
  >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X >{\centering\arraybackslash}X
   }
    \end{tabularx}
    \caption{A few failure cases for our model}
    \label{failure_cases}
\end{figure*}

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


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And if you have a story to share, I'd love to hear it, and possibly share it on the podcast! Send it my way!
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Louis Vuitton, which opened its first store in Beijing in 1992, says the website offers leather goods, small leather goods, shoes, accessories, watches and jewellery, luggage, and the newly launched Les Parfums Louis Vuitton.
The website will be available in 12 cities — Beijing, Shanghai, ChongQing, Chengdu, Guangzhou, Shenzhen, Hangzhou, Nanjing, Shenyang, Dalian, Haerbin, Wuhan. More cities will be added later on.
By Dominique Vidalon; editor: Maya Nikolaeva.


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County Commissioner says Property Taxes to Rise Slightly
Peter Christian Published: September 3, 2020
Your Missoula County tax bill will rise slightly for 2021, says County Commission Chair Josh Slotnick.
Just before the public hearing to go over the 2021 county budget that will set taxes for Missoula County residents, Slotnick provided a preview for KGVO listeners and website viewers.
He said special measures are being taken to provide extra services for those most affected by COVID 19.
What we're looking at right now in terms of costs would be that county taxes would go up by, I think it's $14.10 cents a year on a $300,000 to $350,000 house, and that's well under a buck 50 a month.
"We found that the pandemic is affecting some people much more so than others, and so we're really trying to make sure we meet the needs of the people who are most adversely affected," said Slotnick. "Everybody's affected but especially for those most adversely affected."
The county anticipates approximately $3.4 million in COVID-19-related expenses in fiscal year 2021. The county expects these expenses, which include operation of the county testing clinic, emergency operations center, non-congregate shelter and call center, to be reimbursable through CARES Act funding.  No tax increase is included in the proposed final budget to fund these COVID-19 expenses.
Slotnick said taxes on country residences will rise slightly in 2021.
"What we're looking at right now in terms of costs would be that county taxes would go up by, I think it's $14.10 cents a year on a $300,000 to $350,000 house, and that's well under a buck 50 a month. In addition, that's not just for the new stuff, that's also making sure we can do things like keep the roads repaired and the sheriff's department afloat, and rural fire and all these other things, too."
Slotnick said the recently passed two cents per gallon gas tax will enable the county to keep a lid on county road costs.
"What we did do is we didn't add any money to the road fund from property taxes, imagining that we could make up for any new increases in building new pavement and the time and materials that takes to keep our roads in good shape, we're going to cover all that new costs with gas tax," he said. "This coming year, imagining we're still going to be in this pandemic for a while, we'll keep a close eye on how much that raises, and then going into the following year we'll be able to adjust accordingly."
The commissioners will consider any additional public comment before voting to adopt the final budget during their administrative meeting at 10 a.m. Tuesday, Sept. 8.
Filed Under: County Taxes, COVID Testing, Gas tax, Missoula County Commissioners


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Washington state Sen. Doug Ericksen dies after COVID battle
A Washington state lawmaker died Friday, weeks after he had announced he tested positive for COVID-19 while in Central America – though it wasn't clear if the illness is what killed him.
State Sen. Doug Ericksen, a Republican, had made headlines when he announced he was infected with COVID on a trip to El Salvador. He later asked his colleagues in a written letter for advice on how to get his hands on monoclonal antibodies, which weren't available to him in the country.
He was able to travel to Florida, where he was treated in a hospital. His location at the time of his death wasn't immediately disclosed.
"We are heartbroken to share that our husband and father passed away," his family said in a statement to The Seattle Times.
"Please keep our family in your prayers and thank you for continuing to respect our privacy in this extremely difficult time."
Ericksen, who died at age 52, was a member of the Washington senate since 2011.
It wasn't clear if Ericksen was vaccinated against the coronavirus but he had been a critic of statewide COVID-19 restrictions and introduced a bill to try to block statewide vaccine mandates, the Times stated. He was reportedly an early supporter of the candidacy of former President Donald Trump.
Ericksen had served in the state Senate since 2010 and spent another 12 years in the Washington State House, according to the newspaper.
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