Question: My Pastebin : <http://pastebin.com/PmfDEdEw>

Hi,

I am doing a bus search script and here is a table called "bus\_notavailable" and it has fields as

```
nodate_id(PK)
bus_id(FK)
DD(INT 2)
MM(INT 2)
and YYYY(INT 4)

```

My Question is : Suppose Bus ID is 1 which is not available for these two dates are 15-08-2013 and 26-01-2013 so the record will filtered for this day...

The Nodates Table

```
Nodate_id             bus_id        DD     MM     YYYY
1                     1             15     08     2013
2                     1             26     01     2013 

```

• so bus\_notavailable.DD != '26' its showing me one record

• bus\_notavailable.DD != '11' then its showing me 2 records

I know I did mistakes or don't know the solutions therefore want to find out the way to get rid of, and knew this is a issue of one to many relationship where the 'bus\_notavailable' has two id of the same bus its showing me two records from 'bus' table.

So Do I need a sub-query to achieve this or is there any other methods?

Answer: 
Unfortunately, it's hard to know what "Smart sharpen" does without having access to the Photoshop code. In the meantime, you can try to mix an unsharpen mask, total variation filtering and some color adjustment (perhaps a bit of hue boost).

Also see: <http://www.kenrockwell.com/tech/photoshop/sharpening.htm#>

Update: here is an example of how to remove motion blur from an image:

<https://github.com/stefanv/scikit-image-demos/blob/master/clock_deblur.py>

(the clock image is in the same repository). Unfortunately, scikit-image does not currently have mature deblurring / inverse filtering--but we're working on it!
@StefanvanderWalt
Unfortunately, the Wiener code needs a revamp. I've added an example above on how to motion deblur an image. This method can also be used if you only know your blurring kernel--essentially, you apply the Fourier transform to both your image (f -> F) and your kernel (h -> H), and then return the inverse FFT of (F / H), after making sure that the amplitude is limited in places where H is close to zero. Padding avoids boundary effects, and windowing the image limits oscillations in the reconstruction.
***What values ​​and dimensions the hann window used?***