text:
\section{Introduction}
\begin{figure*}[th!]
  \centering
  \includegraphics[width=1\linewidth]{./flow_new.png}
  \caption{\small \textbf{Image acquisition and reconstruction pipeline.}
                          Presented is the binary image acquisition and reconstruction process.
                          (a) the scene being photographed; (b) the photon flux reaching the binary sensor; (c) the sensor collecting the photons and thresholding the photoelectron count; (d) binary images produced by the sensor; (e) our reconstruction algorithm; (f) the reconstructed image.
}
  \label{pipeline}
\end{figure*}

The resolution of image sensors has been steadily increasing in the past two decades, more or less following Moore's law. Simultaneously, the advent of the cell phone market has placed stringent constraints on the form factor and price of sensors and optical components, creating very strong incentives for camera miniaturization. These two trends are driving the pursuit of smaller and more densely packed pixels. Today, mainstream CMOS sensor technology allows to manufacture pixels in the $1$ micron range, and much smaller pixel sizes are feasible.

However, such a miniaturization comes at the price of pixel quality. Small pixels have an inherently limited full-well capacity and consequently have low SNR and a poor dynamic range. This is already a significant obstacle in mainstream CMOS sensors showing poor capabilities in high dynamic range (HDR) and low light scenes. Furthermore, the resolution of miniature sensors falls below the diffraction limit of existing optics (for comparison, the Airy disk diameter of a lens with $f/2$ at $555$ nm is about $2.7$ $\mu$m).

In \cite{fossum2005sub,nakamura2005image}, Eric Fossum proposed the novel concept of "digital film sensor" composed of sub-micron threshold pixels (\emph{jots}) producing binary outputs. A dense array with billions of jots resembles the traditional photographic film in which silver halide crystals of comparable size form light-sensitive grains, or the human eye in which the role of the binary threshold pixels is played by the cones and rods. When a photon hits a grain, it results in the liberation of a silver atom that in the development process ``exposes" the entire grain. Detailed probability analysis shows that the density of the exposed grains depends non-linearly on the illumination intensity, effectively compressing high dynamic range into a small interval. The response of photoreceptor firing rates in our eyes exhibits similar behaviour.
Thus, if made sufficiently low noise, dense binary pixels can mimic the high dynamic range of photographic films or animal visual systems. A proof of this concept has been shown in the EPFL Gigavision camera \cite{yang2009image} with $0.75$ $\mu$m binary pixels.

While binary sensors seem to be a promising "out-of-the-box" solution for HDR imaging, their output is not directly usable and requires an image reconstruction process. Very low photon count in each individual pixel demands accurate modelling of the underlying Poisson statistics, which renders many mainstream image reconstruction and enhancement approaches inapplicable. Moreover, even the recent low light image enhancement algorithms such as \cite{giryes2013sparsity, toEorNotToE} based on careful modelling of the noise are not directly applicable due to the extreme quantization of the binarized signal.

In \cite{yang2012bits}, a maximum-likelihood approach to image reconstruction from binary pixels was proposed. While the algorithm is based on the minimization of a convex objective, its computational complexity is prohibitive for any time-critical application.

Signal reconstruction from binary measurements has been recently studied in the compressed sensing community \cite{jacques2011robust}. However, the authors used the standard Euclidean data fitting term unsuitable for Poisson statistics.
Sparse representation techniques have also been applied to the closely related problem of \emph{inverse halftoning}, In \cite{son2012inverse} it was proposed to co-train a pair of dictionaries, one suited for representing halftoned images, and the other for the corresponding continues image while enforcing the same representation in both dictionaries. A reconstruction of the continuous valued patch is achieved by plugging the binary patch's representation in its dictionary into the corresponding continuous dictionary. This approach lacks the physical model of the binarization procedure, which is essential in our case. In \cite{mairal2012task} it was demonstrated that task-specific dictionary learning suited for the non-linear inverse problem yields state-of-the-art inverse halftoning results. While, the use of the Euclidean data fitting term and the specific Floyd-Steinberg \cite{floyd1976adaptive} halftoning operator is unsuitable for binary imaging, as we detail in the sequel, the present work follows a similar task-driven learning spirit.

\textbf{Contributions}
In this paper, we consider a variant of a binary thresholded pixel array, for which we formulate the image reconstruction problem as the minimization of a convex objective combining a likelihood term similar to \cite{yang2012bits} and a patch-based sparse synthesis prior. Our objective function can be viewed as a variant of the objective used in \cite{giryes2013sparsity, toEorNotToE} for low light image denoising with a different data fitting term correctly capturing the signal formation model, or as a regularised version of the ML estimator from \cite{yang2012bits}.

We simulate the binary image acquisition process using multiple exposures of a regular Canon DSLR CMOS sensor and show that our approach outperforms the previously proposed ML estimator in terms of image quality. However, since the minimization requires an iterative algorithm, the resulting computational complexity is still prohibitive. To address this bottleneck, we show how to learn a fixed complexity and latency approximation of the image reconstruction operator inspired by the recent works \cite{gregor-icml-10,sprechmann2012learning}. We show that comparable reconstruction quality is achieved at a fraction of the computational complexity of the iterative algorithm. To the best of our knowledge, no efficient signal recovery algorithm exist for non-Euclidian fitting terms.

The rest of the paper is organized as follows: In Section \ref{sec_forward} we describe the acquisition process using a binary sensor, in Section \ref{sec_MLSP} we formulate the reconstruction model, in Section \ref{sec_fastApprox} we show a fast approximation to the reconstruction algorithm. Section \ref{sec_results} is dedicated to experimental evaluation of our algorithm. Finally, Section \ref{sec_conclusions} concludes the paper.

\section{Binary image formation model}
\label{sec_forward}
We denote by $\mbr{x}$ the radiant exposure at the camera aperture measured over a given time interval.
This exposure is subsequently degraded by the optical point spread function denoted by the operator $\mbr{H}$, producing the exposure on the sensor $\mbr{\lambda} = \mbr{Hx}$.
The number of photoelectrons $e_{jk}$ generated at pixel $j$ in time frame $k$ follows the Poisson distribution with the rate $\lambda_j$,
\vspace{-2 mm}
\begin{equation}\label{}
    \mathrm{P}\left( e_{jk} = n \mid \lambda_j \right) = \frac{{e^{ - \lambda_j } \lambda_j^n }}{{n!}}
\end{equation}
A binary pixel compares the accumulated charge against a pre-determined threshold $q_j$\footnote{We assume $q_j$ to be fixed in time. Various CMOS designs with time-varying pixel threshold are possible.}, outputting a one-bit measurement $b_{jk}$ (see Figure \ref{pipeline} for an illustration). Thus, the probability of a single binary pixel $j$ to assume an "off" value in frame $k$ is
\vspace{-1 mm}
\begin{equation}\label{}
    \begin{split}
    p_{j} &= \mathrm{P}(b_{jk}=0 \mid q_j,\lambda_j )=\mathrm{P}(e_{jk} < {q_j} \mid q_j,\lambda_j)
    \end{split}
\end{equation}

\noindent therefore we can write
\begin{equation}\label{}
\mathrm{P}(b_{jk} \mid q_j,\lambda_j) = (1-b_{jk})p_{j} + b_{jk}(1-p_{j}) .
\end{equation}
 Assuming independent measurements, the negative log-likelihood of the radiant exposure $\mbr{x}$ given the measurements $b_{jk}$ is given by
\begin{equation}\label{likelihood}
\ell(\mbr{x} \mid \mbr{B}) = \mathrm{const} - \sum_{j,k} \log \mathrm{P}(b_{jk} \mid q_j, \lambda_j),
\end{equation}
In \cite{EPFL-REPORT-166345}, a maximum-likelihood approach was proposed for solving \ref{likelihood}.

\section{Maximum-likelihood with reconstruction sparse prior}
\label{sec_MLSP}
Standard optimization techniques were used in ~\cite{EPFL-REPORT-166345} to solve problem \ref{likelihood}. In all our experiments problem \ref{likelihood} was solved on the entire image using the trust-region-reflective algorithm from MATLAB optimization toolbox. Since this approach assumes no prior, it requires a large number $K$ of binary measurements in order to achieve a good reconstruction (see Figure \ref{MultipleExpGraph}). Sparsity priors have been shown to give state-of-the-art results in denoising tasks in general, and particularly in low light settings where Poisson noise statistics become dominant \cite{giryes2013sparsity,toEorNotToE}. In the following, we show that by introducing a spatial prior over the reconstructed image patches one may significantly reduce the number of measurements required without hampering the reconstruction quality. Assuming that the radiant exposure $\mbr{\lambda}$ can be expressed by a kernelized sparse representation $\mbr{\lambda} = \mbr{H}\rho(\mbr{Dz})$, where $\mbr{D}$ is a dictionary (e.g. globally trained via k-SVD \cite{aharon2006svd}) and $\rho$ is an element-wise intensity transformation function, we may express the estimator as $\hat{\mbr{x}} = \rho(\mbr{D} \hat{\mbr{z}})$, where
\begin{equation}\label{problemFormulationWithSaprsity}
    \mbr{\hat{z}} = \argmin_{\mbr{z}}{\ell(\rho(\mbr{Dz}) \mid \mbr{B}) + \mu \|\mbr{z}\|_1} ,
\end{equation}
and $\|\mbr{z}\|_1$ denotes the sparsity encouraging $\ell_1$ norm of the coefficient vector $\mbr{z}$. In all the experiments presented in this work, we chose $\rho$ to be the hybrid exponential-linear function introduced in \cite{toEorNotToE},
\begin{equation}
\label{rho}
\rho(x) =
\left\{
    \begin{array}{ll}
        c\exp(x) & x \leq 0 \\
        c(1+x) & x > 0
    \end{array}
\right .
\end{equation}
that was shown to have several advantages for Poisson image reconstruction as it enforces image non negativity constraints, while presenting relatively low Lipschitz constants across all intensity levels.

Since the negative log-likelihood data fitting term is convex (see supplementary material for a proof) with a Lipschitz-continuous gradient, problem (\ref{problemFormulationWithSaprsity}) can be solved using proximal algorithms such as the iterative shrinkage thresholding algorithm (ISTA), its accelerated version \cite{daubechies2004iterative} or FISTA \cite{beck2009fast}.
For completeness, the FISTA algorithm is summarized as Algorithm \ref{algo_fista}, defining $\sigma_\theta$ as the coordinate-wise shrinking function with threshold $\theta$ and $\eta$ as the step size. The gradient of the negative log-likelihood computed at each iteration is given by
\vspace{-1 mm}
\begin{equation}
\frac{\partial\ell}{\partial \mbr{z}} = \mbr{D}^T \rm{diag}(\rho'(\mbr{D}\mbr{z}))\mbr{H}^T \nabla \ell(\mbr{H}\rho(\mbr{D}\mbr{z}) | \mbr{B} ) .
\end{equation}

\begin{algorithm}[h!]
\KwIn{Binary measurements $\mbr{B}$, step size $\eta$}
\KwOut{Reconstructed image $\hat{\mbr{x}}$ }
initialize $\mbr{z}^* = \mbr{z} = 0$, $\beta < 1$, $m_0 = 1$    \\
\For{$t=1,2,...,\mbox{until convergence}$}{

     \textit{//Backtracking} \\
     \While{$\footnotesize{\ell(\mbr{z}^*) \geq \ell(\mbr{z}) + <\mbr{z}^* - \mbr{z},\frac{\partial\ell}{\partial \mbr{z}}> + \frac{1}{2\eta} \| \mbr{z}^* - \mbr{z} \|_2^2 }$}{
     $\eta = \eta\beta$\\
     $ \mbr{z}^* = \sigma_{\mu\eta}(\mbr{z} - \eta\frac{\partial\ell}{\partial \mbr{z}}) $
     }
     \textit{//Step} \\
     \vspace{-1 mm}
     $m_{t+1} = \frac{1+\sqrt{1+4m_t^2}}{2}$\\
     $\mbr{z} = \mbr{z}^* + \frac{m_t-1}{m_{t+1}}(\mbr{z}^* - \mbr{z}) $\\

 }
 $\hat{\mbr{x}}=\rho(\mbr{D}\mbr{z})$
 \vspace{2 mm}
 \caption{FISTA with backtracking.}
\label{algo_fista}
\end{algorithm}
\noindent If the Lipschitz constant $L$ of $\frac{\partial \ell}{\partial \mbr{z}}$ or an upper bound thereof is easy to compute, backtracking can be replaced by a fixed step size $\eta < 1/L$.  The algorithm is reduced to the less efficient ISTA by fixing $m_t = 0$.

\section{Fast approximate reconstruction}
\label{sec_fastApprox}
An iterative solution of (\ref{problemFormulationWithSaprsity}) typically requires hundreds of iterations to converge. This results in prohibitive complexity and unpredictable input-dependent latency unacceptable in real-time applications.
To overcome this limitation, we follow the approach advocated by \cite{gregor-icml-10} and \cite{sprechmann2012learning}, in which a small number $T$ of ISTA iterations are unrolled into a feed-forward neural network, that subsequently undergoes supervised training on typical inputs for a given cost function $f$.
A single ISTA iteration can be written in the form
\begin{equation}
\footnotesize \mbr{z}_{t+1}\ = \sigma_\mbr{\theta}\left( \mbr{z}_{t} - \mbr{W}\rm{diag}(\rho'(\mbr{Q}\mbr{z}_{t}))\mbr{H}^\mathrm{T}\nabla\ell (\mbr{H}\rho(\mbr{A}\mbr{z}_{t}) | \mbr{B} )\right) ,
\end{equation}
where $\mbr{A}=\mbr{Q}=\mbr{D}$, $\mbr{W}=\eta\mbr{D}^T$ and $\mbr{\theta} = \mu\eta\mbr{1}$.
Each such iteration may be viewed as a single layer of the network parameterized by $\mbr{A},\mbr{Q},\mbr{W} \mbox{ and } \mbr{\theta}$, accepting $\mbr{z}_{t}$ as input and producing $\mbr{z}_{t+1}$ as output. Figure \ref{netProp} depicts the network architecture, henceforth referred to as MLNet.

\begin{figure}[th]
  \centering
  \includegraphics[width=1\columnwidth]{./MLNet.pdf}
  \caption{\small\textbf{MLNet architecture.}
A small number $T$ of ISTA iterations are unrolled into a feed-forward network. Each layer applies a non-linear transformation to the current iterate $\mbr{z}_t$, parametrized by $\mbr{H},\mbr{A},\mbr{Q},\mbr{W}$ and $\mbr{\theta}$. Training these parameters using standard back propagation on a set of representative inputs allows the network to approximate the output of the underlying iterative algorithm with much lower complexity.  }
  \label{netProp}
\end{figure}

Network supervised training is done by initializing the parameters as prescribed by the ISTA iteration and then adapting them using a stochastic gradient approach which minimizes the reconstruction error $\mathcal{F}$ of the entire network. We use the following empirical loss
\vspace{-1 mm}
\begin{equation}
\label{general_loss}
\mathcal{F} = \frac{1}{N}\sum_{n=1}^{N}f(\mbr{x}_n^*,\hat{\mbr{z}}_T(\mbr{B}_n),\mbr{D}) ,
\end{equation}
which for a large enough training set, $N$, approximates the expected value of $f$ with respect to the distribution of the ground truth signals $\mbr{x}_n^*$. Here, $\hat{\mbr{z}}_T(\mbr{B}_n)$ denotes the output of the network with T layers given the binary images $\mbr{B}_n$ produced from $\mbr{x}_n^\ast$ as the input.

Similarly to \cite{gregor-icml-10} the output of the network and the derivative of the loss with respect to the network parameters are calculated using the forward and back propagation, summarized as Algorithms \ref{algo_front_prop} and \ref{algo_back_prop}, respectively. Practice shows that the training process allows to reduce the number of iterations required by about two orders of magnitude while achieving a comparable reconstruction quality (see Figure \ref{NN_complexity}). To the best of our knowledge, this is the first time a similar strategy is applied to reconstruction problems with a non-Euclidean data fitting term.

\begin{algorithm}[h!]

 \KwIn{Number of layers $T$,$\mbr{\theta}$,$\mbr{Q}$,$\mbr{D}$,$\mbr{W}$,$\mbr{A}$}
 \KwOut{Reconstructed image $\mbr{\hat{x}}$, \\ \hspace{13 mm}auxiliary variables $\{\mbr{z_t}\}_{t=0}^T$,$\{\mbr{b_t}\}_{t=1}^T$ }

initialize $\mbr{z}_0=0$

 \For{t = 1,2,\dots,T}{
 $\footnotesize{\mbr{b}_t = \mbr{z}_{t-1} - \mbr{W}\mr{diag}(\rho'(\mbr{Q}\mbr{z}_{t-1}))\mbr{H}^\mathrm{T}\nabla \ell (\mbr{H}\rho(\mbr{A}\mbr{z}_{t-1}))}$\\
 $\footnotesize{\mbr{z}_t = \sigma_\mbr{\theta}(\mbr{b}_t)}$
 }
$\footnotesize{\mbr{\hat{x}}=\rho(\mbr{D}\mbr{z}_T}$)
\vspace{2 mm}
 \caption{Forward propagation through MLNet.}
\label{algo_front_prop}
\end{algorithm}

\begin{algorithm}[h!]

 \KwIn{Loss $\mathcal{F}$, outputs of \ref{algo_front_prop}: $\{\mbr{z_t}\}_{t=0}^T$,$\{\mbr{b_t}\}_{t=1}^T$}
 \KwOut{Gradients of the loss w.r.t. network parameters $\footnotesize{\mbr{\delta W}}$,$\footnotesize{\mbr{\delta A}}$,$\footnotesize{\mbr{\delta Q}}$,$\footnotesize{\mbr{\delta \theta}}$}
 \vspace{1 mm}
 initialize $\footnotesize{\mbr{\delta W}^T=\mbr{\delta A} = \mbr{\delta Q}=\mbr{0}}$,$\footnotesize{\mbr{\delta\theta}=\mbr{0}}$,$\footnotesize{\mbr{\delta z}_T = \frac{d \mathcal{F}}{d \mbr{z}_T}} $\\

 \For{t = T,T-1,\dots,1}{

 $ \footnotesize{\mbr{a}^{(1)} = \mbr{Az}_{t-1}}$ \\
 $ \footnotesize{\mbr{a}^{(2)} = \mbr{Qz}_{t-1}}$ \\
 $ \footnotesize{\mbr{a}^{(3)} = \mbr{Az}_{t}}$ \\
 $ \footnotesize{\mbr{a}^{(4)} = \mbr{Qz}_{t}}$ \\
 $ \footnotesize{\mbr{a}^{(5)} = \mbr{H} \mr{diag}(\rho'(\mbr{a}^{(2)}))}$ \\

 $ \footnotesize{\mbr{\delta b} = \mbr{\delta z}_t \mr{diag}(\sigma_\mbr{\theta}'(\mbr{b}_t)) } $\\

 $ \footnotesize{\mbr{\delta W} = \mbr{\delta W} - \mbr{\delta b} \nabla\ell(\mbr{H}\rho(\mbr{a}^{(1)}))^T \mbr{a}^{(5)} } $ \\

 $ \footnotesize{\mbr{\delta A} = \mbr{\delta A} - \mr{diag}(\rho'(\mbr{a}^{(1)}))\mbr{H}^T \nabla^2 \ell(\mbr{H}\rho(\mbr{a}^{(1)}))^T \mbr{a}^{(5)} \mbr{W}^T \mbr{\delta b}_t \mbr{z}_{t-1}^T  }$\\

 $ \footnotesize{\mbr{\delta Q} = \mbr{\delta Q} - \mr{diag}(\mbr{H}^T \nabla\ell(\mbr{H} \rho(\mbr{a}^{(1)})))\mr{diag}(\rho''(\mbr{a}^{(2)}))\mbr{W}^T \mbr{\delta b} \mbr{z}_{t-1}^T}$\\

 $ \footnotesize{\mbr{\delta \theta} = \mbr{\delta \theta} - \mbr{\delta z} \frac{\partial \sigma_\mbr{\theta} (\mbr{b}_t)}{\partial \mbr{\theta}}}$ \\

 $ \footnotesize{ \mbr{F} = \mbr{W} \mr{diag}(\rho'(\mbr{a}^{(4)})) \mbr{H}^T  \nabla^2\ell(\mbr{H}\rho(\mbr{a}^{(3)}) \mbr{H} \mr{diag}(\rho'(\mbr{a}^{(3)})\mbr{A}))  }$ \\
 $ \footnotesize{ \mbr{G} = \nabla\ell(\mbr{H}\rho(\mbr{a}^{(3)})^T\mbr{H} \mr{diag}(\rho''(\mbr{a}^{(4)})) \mr{diag}(\mbr{W}^T \mbr{\delta b}^T)\mbr{Q}  }$ \\

 $ \footnotesize{\mbr{\delta z}_{t-1} = \mbr{\delta b}^T ( \mbr{I} - \mbr{F} ) - \mbr{G}      }   $ \\

 }
\vspace{2 mm}
 \caption{Gradient computation by back propagation through MLNet. We follow the shorthand notation common in the machine learning literature, denoting by $\delta*$ the gradient of the scalar loss $\mathcal{F}$ w.r.t. the network parameter $*$. Note that $\mbr{\delta \mbr{D}}$ is calculated separately as it only depends on the last iteration of the network.}
\label{algo_back_prop}

\end{algorithm}

\section{Results}
\label{sec_results}
\subsection{Low light} \label{lowlight}
This experiment emphasizes the superiority of MLNet over an unregularized ML reconstruction in low light conditions. Furthermore, it illustrates the tradeoff between reconstruction quality and computational complexity.

As a low-resolution ground truth we used the Lena gray-level image normalized to the low light range $[0,10]$. A high-resolution binary image was created in the following way: First, a photon flux average rate image was created by up-sampling and low-pass filtering the ground truth image using a $2D$ Gaussian filter with a standard deviation of $3$ and an up-sampling factor of $5$. Then, an acquisition process was simulated as a realization of the Poisson probability distribution of the given average rate image followed by binarization using a fixed $5\times 5$ threshold pattern consisting of the values $q_i\in\{1,...,10\}$ distributed uniformly and tiled over the entire image.

An image was reconstructed from the binary measurements using the unregularized ML and using MLNet with several depths $T$ operating on $8 \times 8$ overlapping patches. The function $\rho$ was set according to \ref{rho} with c=10.

MLNet was trained using stochastic gradient decent optimization on a disjoint set of $30,000$ $8 \times 8$ image patches normalized to the same range. In each iteration the descent direction was calculated on a random minibatch of $1,000$, low-resolution ground truth patches and their corresponding randomly generated high-resolution binary measurements. The optimization over the parameters was done via round-robin \textit{i.e.} the parameters were optimized sequentially in a circular manner. A validation set was used to prevent over-fitting.
The loss objective $f$ minimized during the training process was the standard squared error,
\begin{equation}
f = \frac{1}{2}\| \mbr{x}_n^* - \rho(\mbr{D} \mbr{z}_T(\mbr{B}_n)) \|_2^2,
\end{equation}

\noindent where $\mbr{x}_n^*$ denote the training ground truth patches.

Figure \ref{Lena} compares the unregularized ML reconstruction to the reconstruction of MLNet with $T=8$ layers without patch overlap. As can be seen MLNet significantly outperforms the reconstruction obtained by the unregularized ML reaching $27.5$ dB PSNR compared to only $16.6$ dB.

\begin{figure}
    \centering
        \begin{tabular}{ c c }
            \includegraphics[width = 0.2\textwidth]{./LenaEye_binary.png} &
            \includegraphics[width = 0.2\textwidth]{./LenaEye.png} \\
            (a) Binary image & (b) Ground Truth \\
            \includegraphics[width = 0.2\textwidth]{./LenaEye_ML.png} &
            \includegraphics[width = 0.2\textwidth]{./LenaEye_MLNet.png} \\
            (c) Unregularized ML  & (d) MLNet \\
          \end{tabular}   \\
\vspace{2 mm}
    \caption{\small \textbf{Low light reconstruction.}
        Lena's image was normalized to $[0,10]$ from which four input binary images were emulated.
        Depicted is a zoomed in fragment of the images: (a) input binary image; (b) low-resolution ground truth; (c) unregularized ML reconstruction (PSNR=$16.6$ \rm{dB}); (d) MLNet reconstruction (PSNR=$27.5$ \rm{dB}). MLNet performs better both quantitatively and qualitatively. The reader in encouraged to zoom-in on the images.
}
    \label{Lena}
\end{figure}

In order to evaluate the tradeoff between reconstruction quality and computational complexity, in Figure \ref{NN_complexity} we compared the performance of MLNet with different depths with the reconstruction quality achieved by ISTA and FISTA terminated after a comparable number of iterations (thus, the compared reconstruction algorithms have approximately the same complexity). As a reference,  we also show the unregularized ML reconstruction. We observe that MLNet produces acceptable reconstruction quality already for $T=4$ layers, which takes FISTA around $200$ iteration (at least $50$ times higher complexity) to achieve. Similar speedup was observed consistently in other experiments not reported here due to lack of space.

\begin{figure}[]

\includegraphics[width = 1\columnwidth]{./NN_VS_FISTA.eps}
\caption{\small \textbf{Reconstruction accuracy under budgeted computational complexity.} The plot shows the reconstruction quality for the iterative ISTA and FISTA solvers with the ML data fitting term when terminated after a given number of iterations, and the proposed MLNet with an equivalent number of layers. As a reference, the performance of the unregularized ML is shown. Iteration $1$ represents the initialization for all algorithms.}
\label{NN_complexity}
\end{figure}

\subsection{Multiple exposures} \label{multipleExposures}
In this experiment, we reconstructed a synthetic static low light scene using a sequence of multiple exposures. As is often the case in estimation problems, as the number of measurements increases the effect of regularization slightly degrades the image quality compared to the unregularized estimator. Figure \ref{MultipleExpGraph}, summarizing the experiment described in this section, shows that the number of measurements required by the unregularized ML estimation to outperform reconstruction with a sparse prior is unacceptably high for most practical purposes. In addition, it is evident that a BM3D (\cite{makitalooptimal},\cite{makitalo2009inversion}) based method outperforms the ML estimator but is inferior to its regularized version.

The scene used for the experiment was a low light image with gray values normalized to the range $[0,10]$. A high resolution average rate image was created as in Section \ref{lowlight} by up-sampling the ground truth image by a factor of $3$, followed by a Gaussian filter with a standard deviation of $1.5$. $K$ different binary images were created by generating independent photon count images via a realization of the Poisson process followed by binarization. Binarization was performed using a threshold pattern of size $3 \times 3$ containing the threshold values $q_i\in\{1,...,9\}$  tiled regularly over the entire image. $c=10$ was selected for function (\ref{rho}). FISTA was run until convergence with the regularization weight $\mu = 4$ and a backtracking procedure to determine the step size $\eta$ at each iteration.
Note that the data fitting term (\ref{likelihood}) increases linearly with $K$, therefore, in practice keeping $\mu$ constant reduces the regularization weight as $K$ increases.

As a comparison to Poisson denoising algorithms we reconstruct the image using BM3D. As each pixels value is a Bernoulli variable summing over $K$ exposures yields a Binomial variable that can be approximated as a Poisson distribution. This image can be denoised using the Poisson adapted version of BM3D. The underlying photon flux rate $\lambda_j$ can be inferred from $p_j$ and $q_j$, then spatial binning can be applied according to the spatial oversampling of the binary sensor.

Figure \ref{MultipleExpGraph} shows the reconstruction PSNR of BM3D, regularized ML solved via FISTA, with and without overlapping patches, and its unregularized counterpart for different values of the temporal oversampling $K$. In cases where overlap was used the patch shift was four pixels and the overlapping patches were averaged. Examples of reconstructed images for $K = 1$ and $K=32$ are presented in Figure \ref{MultipleExpImages}.

It is evident from the figures that the sparse prior enables a significant improvement in reconstruction quality for a vast range of temporal oversampling values. While the regularized solution reaches a PSNR value of $23.2$ \rm{dB} using a single exposure, its unregularized counterpart reaches similar reconstruction accuracy only after about $200$ exposures. Using $K=32$ exposures, regularized ML achieves $29.9$ \rm{dB} PSNR and the reconstructed image looks visually satisfying while the unregularized ML reconstruction looks poor yielding only $16.9$ \rm{dB} PSNR proving the superiority of a sparse reconstruction for practical reasons. It is worth mentioning that the intersection of reconstruction quality with and without a sparse prior is at around $2000$ exposures which is well beyond any practical limit while assuming a static scene. Furthermore, we can conclude that taking into account the correct statistics of the problem results in a better reconstruction compared to a general Poisson denoising technique.

\begin{figure}
    \centering
        \begin{tabular}{ c c c}
            \hspace{-2 mm}\includegraphics[width = 0.15\textwidth]{./car_fista_K1.png} & \hspace{-4 mm}
            \includegraphics[width = 0.15\textwidth]{./car_ml_K1.png} & \hspace{-3 mm}
            \includegraphics[width = 0.15\textwidth]{./car_bm3d_K1.png} \\
            \hspace{-2 mm}(a) \small{Reg ML, $K=1$}  & \hspace{-3 mm} (b) \small{ML, $K=1$} & \hspace{-4 mm} (c) \small{BM3D, $K=1$}\\

            \hspace{-2 mm}\includegraphics[width = 0.15\textwidth]{./car_fista_K32.png} & \hspace{-4 mm}
            \includegraphics[width = 0.15\textwidth]{./car_ml_K32.png} & \hspace{-3 mm}
            \includegraphics[width = 0.15\textwidth]{./car_bm3d_K32.png} \\
            \hspace{-2 mm}(d) \small{Reg ML, $K=32$}  & \hspace{-3 mm} (e) \small{ML, $K=32$} & \hspace{-4 mm} (f) \small{BM3D, $K=32$}\\
            \end{tabular}   \\
          \vspace{2 mm}
    \caption{\small \textbf{Reconstruction from multiple exposures.}
        (a), (b) and (c) are the reconstructions of Regularized ML, ML and BM3D with binning using a single binary image ($K=1$);
        (d), (e) and (f) are the reconstructions of Regularized ML, ML and BM3D with binning using ($K=32$) binary images.

}
    \label{MultipleExpImages}
    \vspace{-1em}
\end{figure}
\begin{figure}[]

\includegraphics[width = 1\columnwidth]{./car_FISTA_VS_ML.eps}
\caption{\small \textbf{Reconstruction quality for different amounts of temporal oversampling $K$.} The plot shows the effect of the number of exposures $K$ over the reconstruction quality for the unregularized and regularized ML estimators (the latter solved using FISTA) as well as the reconstruction using BM3D with binning.}

\label{MultipleExpGraph}
\vspace{-1em}
\end{figure}

\subsection{HDR imaging emulation} \label{HDR}
This experiment applies our method to HDR images while demonstrating the feasibility of real-time image reconstruction from binary measurements using MLNet, with inputs emulated using a DSLR sensor.

We present the performance of MLNet on real captured scenes with a five orders of magnitude dynamic range. In the absence of an actual sub diffraction limit binary sensor, we emulated its functionality using a Canon DSLR sensor. The spatial oversampling effect was simulated by defocusing the captured frames (the amount of defocusing was set to fit the desired spatial oversampling of $13 \times 13$), thus resulting in a reconstructed image that is smaller by the upsampling factor compared to the native camera resolution. We found that setting the ISO value to $200$ results in a gain of one gray level per photoelectron for pixels belonging to the \rm{G1} Bayer channel, thus allowing direct photoelectrons counting from the sensor raw images (after subtracting the dark value). As the dynamic range of a single RAW image is about four orders of magnitude, in order to capture the entire dynamic range of the scene, a sequence of images with multiple exposure times was taken, starting with the longest exposure and replacing only the saturated content using information from shorter exposures. In order to preserve the underlying Poisson statistics generated by the source light field, we used the additivity property of independent Poisson variables taking exposure times ranging between $0.01$ and $0.2$ seconds to produce an input image of the photoelectron flux with a total dynamic range of five orders of magnitude. A low resolution ground truth image was created by averaging $10$ such photoelectron count images and down-sampling, as shown in Figure \ref{hdr}(a). The scenes presented were set in a dark room and were lighten with a LED flash light producing a dynamic range $[0,10^5]$.

The binary image was generated using a threshold pattern selected according to the following rationale: Due to the fact that the variance of a Poisson distribution is equal to its average rate, a single binary pixel would show informative behavior in the interval $\left[q-2\sqrt{q},q+2\sqrt{q}\right]$. The number of threshold values required in order to cover the range $[0,10^5]$ with such intervals is approximately $155$, explaining the choice of the spatial oversampling ratio to be $13 \times 13$. The remaining threshold values were spread over the lower region of the dynamic range using an analogous technique. The resulting binary image is depicted in Figure \ref{hdr}(second and fourth rows of the column (a)).

The dictionary used for the regularization term in the regularized ML estimator and the initialization of MLNet was a $k$SVD dictionary with $257$ atoms of size $8 \times 8$ that was trained on the \rm{log} intensity of patches from the Stanford HDR dataset \cite{xiao2002high}.
 A regularization weight of $\mu=50$ was empirically chosen for FISTA using a small validation set from \cite{xiao2002high}. In order to reduce the number of FISTA iterations, we altered Algorithm \ref{algo_fista} by resetting the step size $\eta$ to its initial value every five iterations. Our experience shows that this allows faster convergence with our non-Euclidean data fitting terms, as it allows longer steps on sub-domains where the Lipschitz constant of the gradient of $\ell$ is smaller. BM3D was used as explained in section \ref{multipleExposures}.

As in the low light experiment detailed in Section \ref{lowlight}, MLNet was trained using data similar to the data used for the $k$SVD dictionary training process. We trained MLNet to have $T=25$ recurrent layers with $c=10^5$ using a stochastic gradient optimization, similar to the one described in Section \ref{lowlight}, only using a smaller minibatch size due to memory limitations. In this experiment we used an objective function of the form
\vspace{-1 mm}
\begin{equation}\label{loss_hdr}
f = \frac{1}{2}\| \rm{log}(1+\mbr{x}_n^*) - \rm{log}(1+\rho(\mbr{D} \mbr{z}_T(\mbr{B}_n))) \|_2^2
\end{equation}

\noindent which we found to be a more meaningful error criterion for HDR images. Reconstruction results using FISTA and MLNet are presented in Figure \ref{hdr}. FISTA was run without patch overlap and MLNet was run with full overlap, followed by patch averaging. Due to the images being HDR we chose to present them in a \rm{log} scale. In addition, difference images between the \rm{log} of the ground truth images and the \rm{log} of the reconstructions are also presented. Interestingly, these results show that training on synthetic data from Stanford HDR dataset results in a neural net that performs well on real world data captured by a DSLR sensor.

Reconstruction time required per algorithm for Figure \ref{hdr}(third row, column (a)) are presented in Table \ref{recon_time_table} showing that MLNet with $T=25$ recurrent layers offers a speedup between three to four orders of magnitude compared to the regularized ML reconstruction via FISTA. Note that the timing reported for MLNet in the seconds range was obtained on a regular Intel Core i7 CPU. MLNet is amenable to heavy parallelization (at least, at the patch level) and is hardware-friendly because of its regular fixed-latency data flow. We are currently developing an FPGA prototype demonstrating a video-rate real time reconstruction. It is evident that Poisson denoising algorithms such as BM3D fail once the number of exposures is low and the scene is of high dynamic range. Due to lack of space and poor quality we chose not to show these reconstructions in Figure \ref{hdr}.

Although the images reconstructed by MLNet offer high PSNR values they seem to be slightly blurred. While we currently do not fully understand the source of this blur and whether it can be mitigated by more sophisticated network architectures, we believe that MLNet offers an interesting tradeoff between reconstruction quality and computational complexity for real world applications and HDR imaging from a single binary image.

\begin{figure*}[h]
    \centering
        \begin{tabular}{ c c c c }
           \includegraphics[width = 0.23\textwidth]{./pink_log_gt.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_FISTA.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_MLNET.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_ML.png} \\

           \includegraphics[width = 0.23\textwidth]{./pink_binary.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_FISTA_err.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_MLNET_err.png} &
           \includegraphics[width = 0.23\textwidth]{./pink_log_ML_err.png} \\
            & PSNR=$41$ \rm{dB} & PSNR=$40$ \rm{dB} & PSNR=$24.4$ \rm{dB} \\
            & & & \\

           \includegraphics[width = 0.23\textwidth]{./cartman_log_gt.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_FISTA.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_MLNET.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_ML.png} \\

           \includegraphics[width = 0.23\textwidth]{./cartman_binary.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_FISTA_err.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_MLNET_err.png} &
           \includegraphics[width = 0.23\textwidth]{./cartman_log_ML_err.png} \\
            & PSNR=$35.6$ \rm{dB} & PSNR=$35.5$ \rm{dB} & PSNR=$17.9$ \rm{dB} \\
           (a) & (b) & (c) & (d) \\

          \end{tabular}   \\
          \vspace{2 mm}
    \caption{\small \textbf{High dynamic range reconstruction} of the Lego Android (top rows) and Cartman (bottom rows) images. Column (a) shows the ground truth low resolution image (first and third rows) and the high resolution binary input (second and fourth rows). Column (b) presents regularized ML reconstruction via FISTA with no patch overlap and the difference image with the ground truth. Columns (c) and (d) present the same for MLNet and the unregularized ML algorithms respectively. Note that all images are presented on a logarithmic scale on which the PSNR is calculated. It is evident that the sparse prior enables much better reconstructions of real HDR scenes captured using a DSLR camera. We encourage the reader to zoom-in on the images.}
    \label{hdr}
\end{figure*}

\begin{table}
\begin{tabular}{| l | c | c | c | c |}
        \hline
        Algorithm & BM3D & ML & FISTA & MLNet \\
        \hline
        Time & 20 min & $6.2$ hrs & $2.8$ hrs & $4$ sec \\
        PSNR [\rm{dB}] & 6.7 & $17.9$ & $35.6$ & $35.5$ \\
        \hline
\end{tabular}
\vspace{2 mm}
\caption{\small \textbf{HDR image reconstruction time comparison}. A comparison of the Cartman image reconstruction time. FISTA and MLNet were both run without patch overlap.}
\label{recon_time_table}
\vspace{-1em}
\end{table}

\section{Conclusions and future work}
\label{sec_conclusions}
In this work we discussed image reconstruction from a set of dense binary measurements, motivated by recent advances in the sensing hardware. We demonstrated the superiority of the proposed regularized ML reconstruction over "vanilla ML" as well as Poisson denoising algorithms such as BM3D, showing that the regularized version achieves similar performance as its unregularized counterpart with about three orders of magnitude less measurements. Taking computational complexity into account, we also showed how to compute an efficient and hardware-friendly approximation to the reconstruction algorithm. Promising results were shown on synthetic data as well as on data emulated using multiple exposures of a regular CMOS sensor.
It is worthwhile noting that the training loss function (\ref{general_loss}) allows great freedom of choice, including fully unsupervised or semi-supervised training regimes. While we clearly observed non-negligible effects of such a choice on the reconstruction quality, in this work we opted for the standard MSE criterion, deferring the detailed study of the loss functions to future work.
We would like to state that the proposed techniques have the same limitations of approaches based on a universal dictionary as opposed to a prior trained from the data themselves. They also suffer from known shortcomings of patch-based approaches that average reconstructed overlapping patches: while each of the patches admits the prior, their average might not. In future work, we intend to explore ways of overcoming these limitations.

\clearpage
{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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Excellent insights showcased. It's good to see so many recommendations and insights in just 1 month. Good job team.
The results in all areas were very promising and we are already in the process of making some significant changes.
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The weekend is part of a balanced diet.
Your Perks: 1) Three bottles of wine, tailored to your tastes and delivered to your door. 2) A high-tech scale that measures your weight and body fat, and sends the data to your phone. 3) A pair of wireless Swedish-made speakers. In the Swedes you trust.
Elvis Presley would be 80 today. So to celebrate, Alamo Drafthouse is showing Jailhouse Rock and serving Elvis's favorite snack—peanut butter, banana and bacon sandwiches. What they're saying is that Elvis knew how to snack.
Get a Haircut. Have Some Whiskey.
The Fly Barbershop guys—you know, that scissor-wielding crew in an Airstream—are now popping up at Rye 51 every Wednesday, Thursday and Saturday. So swing by or make an appointment for a haircut, then peruse some American-made goodness. Whiskey counts.
Big game this Sunday, so you might as well watch it on a really big screen. Try the Granada. They're showing the Cowboys vs. Packers on three HD projectors with concert sound, so it's almost like you're watching it live in Green Bay. Except you'll keep all your toes.
Death & Co. It's an NYC bar and a real pinnacle of cocktailery, and those guys are debuting their new book Tuesday at Midnight Rambler. You should go to the launch to score a copy. But only if you enjoy drinking delicious drinks. Anyway, think about it.
Eat Hot Dogs. Listen to Rush. Improve Your Life.


Long-eared Owls are secretive, nocturnal, and superbly camouflaged. One good way to find them is to listen at night in spring and summer for their long, low hoots. During winter these owls often roost in large numbers, and this can make them easier to find. Methodically search pine stands or shelterbelts near grassland or pasture for roosting owls, often close to the tree trunk among dense branches. Also look along the ground for pellets (gray, roughly oval cylinders of regurgitated fur, feathers, and bone). If you find a large number of these, you may be under a roost tree. Long-eared Owl pellets are typically 2-3" long, while pellets of other owls found in such situations are either larger and less elongate (Great Horned Owl) or smaller and rounder (Northern Saw-whet Owl). Also scan the ground and lower branches for extensive whitewash (bird droppings), which can also indicate recent roosting by owls.
Long-eared Owls may nest in artificial baskets and open-fronted nest boxes.
The hoot of the male Long-eared Owl can sometimes be heard up to 1 kilometer (0.7 mi) away.
Like other owls, the Long-eared has a body adapted for silent flight and precision hunting. Flight feathers with fringed edges and downy surfaces mute the sound of the owl's passage through air. The owls gain incredible hearing from their asymmetrically placed ear openings and large, sound-catching facial disks.
In 1994, a researcher discovered a nesting Cooper's Hawk incubating two Long-eared Owl eggs along with three of its own. The hawk had probably usurped the nest from the owl. Another researcher documented three cases of Long-eared Owls appropriating nests that had been recently built by American Crows.
Long-eared Owls swallow their prey whole and then regurgitate the indigestible parts in pellets, usually one per day. If you find these pellets they're fascinating to pick through, full of tiny animal bones and fur. Some biologists collect these pellets and use them to learn about owl diets.
In addition to the North American and Eurasian populations, isolated groups of Long-eared Owls occur in North and East Africa, the Azores, and the Canary Islands. While this owl's biology has been extensively studied in the U.S. and Europe, little is known about it in other parts of its range.
The oldest Long-eared Owl on record was at least 12 years, 1 month old. It had been banded in New York and was later found in Ontario, Canada.


text:
Engineer praises natural building materials in lecture
Houses that are built with clay and mud cool the climate significantly, engineer Bruce King said.
By Mardio Lattimore
sac-ranger@alamo.edu
Complete deforestation of the Earth's ozone layer and ecosystem may be more likely to happen sooner than people think, yet people have all the tools to stop this, structural engineer Bruce King said Oct 11 in McCreless Hall.
He spoke to an audience of about 30 in an event sponsored by the American institute of Architects, Sinkin Eco Centro, and the Earth and Construction Initiative.
King is the author of three books called "Buildings of Earth and Straw," "Making Better Concrete" and "Design of Straw Bale Buildings."
He is not only the co-founder, but the director of the Ecological Building Network and has also taught ecological building practices in international settings.
King showed slides as he discussed earth-sourced architecture.
He said that the plastic and concrete being used for construction hurts the ecosystem, but materials such as sand, clay, and wood inhibit global warming.
He said that the rapid number of buildings being constructed in the U.S. to accommodate the rising population is equivalent to New York City being built every 35 days.
To help introduce natural earth buildings in the city, King said it would benefit society greatly because construction workers would have "a very simple supply change."
The projects construction workers build this year will have a three out of four chance of hurting the environment by putting too much carbon in the air, he said.
This damages the ozone layer when concrete is heated up before being spread, King said.
An evolving replacement for concrete and metal is timber and straw, although he acknowledged using too much could be damaging to the forest.
"Will this transition wreck the forest? It could if we don't do it right," King said.
Europeans are using forest-stewardship-certified-wood beams with straw being packed into the interior to make large buildings.
He said this has been worth the investment because it is easier to build with and more durable against environmental weathering.
King said plastic and refrigerates damage the air when people burn them or leave them lying around in stockpiles because they are made up of a material more harmful than carbon dioxide.
Clay is as hard as concrete but requires less heat than concrete when being molded, which means it puts less carbon in the air and is weather resistant, he said.
"Clay binds things together, and that's what makes it work," king said.
For more information, visit his website at https://bruce-king.com/ or call Eco Centro at 210-486-0417
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redpajama_set_name: RedPajamaCommonCrawl


Chicago has lost two straight – a 6-3 loss to Los Angeles on Saturday and a 5-2 defeat to San Jose on Sunday. The Hawks have allowed at least five goals in 18 games this season, tied for third most in the NHL.
The Blackhawks beat the Sabres on February 1, 7-3. They are 13-1-0 against the Sabres since 2010-11, their best record against any opponent. The Sabres' 1-10-3 record against Chicago in that time is their second-worst record against any opponent (1-10-2 versus the Blues).
Jack Eichel scored twice against Edmonton on Monday, matching his career high with 25 goals on the season. He needs two more to reach 100 career goals – the last skater to hit 100 career goals in his first four seasons in a Sabres jersey was Thomas Vanek (144) from 2005-06 to 2008-09.
Erik Gustafsson lit the lamp against San Jose and has 10 points (1g, 9a) in his last six games. He has 48 points on the season after having 16 points all of last season – that 32-point increase is the second largest in the NHL behind Toronto's Kasperi Kapanen (+33, minimum 30 games each season).


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text:
Marshalls revenue down as factories close
Capacity cut by 20% and managed installations division closes as downturn hits manufacturing and landscaping firm
Manufacturing and landscaping firm Marshalls suffered a fall in revenue in 2009 amid difficult economic conditions that forced it to reduce its operating capacity.
The company said in its interim management statement that its revenue for the four months to 30 April was £103m, down from £135m in 2008.
It permanently closed four manufacturing sites during the period as the downturn took hold, reducing its production capacity by around 21%.
The manufacturing and landscaping firm noted subdued" activity in the public sector and commercial market
The group's managed installations business was also closed in a bid to save fixed costs, and it ceased commissioning of ready-to-use mortar sites.
Marshalls said it had taken decisive action to conserve cash resources and added that capital expenditure plans had been curtailed. It said lower taxation and pension payments would also conserve cash in the short term.
In its outlook, the group said that the public sector and commercial market, which represented 59% of group revenue in 2008, had become "subdued", but added that the domestic market had recently performed more strongly than its expectations.
Marshalls axes up to 200 factory jobs
Paving stone maker Marshalls is shutting two factories with the loss of 200 jobs as the collapse in demand from the new-build housing market continues to plague manufacturers.
Materials sales slip 10% at Marshalls
Supplier hit as commercial projects halted and housing sector demand falls 15%
Cost cutting reduces Marshalls debt by £37m
Firm closed factories and raised £4m in rights issue in response to 19% fall in revenue


This free live wallpaper is ideal for all Jews. It is a perfect decoration for a phone screen since it is inspired by various beautiful images of Jewish religion, including the apocalypse. Enjoy this original live wallpaper and show your true "religious beliefs" in Judaism! Don't waste your time – download the free app immediately!
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The first writings labeled as apocalyptic revelations in Jews were produced in Palestine in the third and second centuries BCE (Cohn 163). Like the Book of Revelation, which would recycle many of the apocalyptic motifs set down in the Hebrew Bible, the works are difficult and full of symbolism, but from them one can gather three basic eschatological concepts: the new world to come, the resurrection of the dead human body, and the Messiah (Novak 114).
The Hebrew word Mashiach (or Moshiach) means anointed one, and refers to a mortal human being. Within Judaism, the Messiah is a human being who will be a descendant of King David continuing the Davidic line, and who will usher in a messianic era of peace and prosperity for Israel and all the nations of the world.
Hebrew revelations cast a wide net, drawing on biblical prophecy (though Jewish apocalypticism predates Christianity, they later developed alongside one another), Mesopotamian and Zoroastrian traditions, and ancient Israelite myths. Though the visions change, the basic familiar framework remains the same: the dead are resurrected; every human being are judged and meted out his proper reward; God's enemies are destroyed; the Kingdom of Heaven is established on earth.
Personal and social eschatology are closely tied together; the journey of the individual soul is not complete until the world's life cycle is finished, as well. Humans are resurrected and judged, and it is only after the world has ended and God's Kingdom established that individuals will enjoy the fruits of their righteousness in life.
Enjoy having this amazing religious background inspired by Judaism and the people of Israel!
This app is ad supported. It means that if you install it, you may get shortcut icons on your phone background which are actually links to sponsored apps. If you want, feel free to remove them anytime without affecting the application in any way.
You may also receive certain notifications about similar applications. However, if you don't want to get them, you can opt out at http://www.airpush.com/optout and http://opt.leadbolt.com/ or do it directly from the icon where it is available.


I am sorry if this information is already out there somewhere or previously discussed, but would appreciate an answer regardless!
Is there a list anywhere, perhaps on the TDR webpage (I've looked already but didn't see one), that lists all of the TDR approved outcross dogs, whether they have been used/bred yet or not? I saw the breeding dogs lists, but it looks like it is just that — dogs that have been bred and produced offspring. Or am I incorrect and this also also includes approved outcrosses which have not yet been bred?
Ah! Thank you! Using the database and looking at the other breeds was what I was looking for! I didn't know that was an option.


text:
The Rise of Skywalker: A Disappointing End to the Skywalker Saga
Posted byethanmcintyre May 6, 2020 Posted inUncategorizedTags:episode ix, rise of skywalker, star wars
It was Star Wars Day (May 4) on Monday, so I've been in a Star Wars mood this week. It seemed like a good time to go over the most recent entry in the franchise, The Rise of Skywalker.
I love Star Wars. I have since I was a kid. I was already a die-hard fan by the time the prequels came out; for all their flaws, I was always excited to go see them in the theater. When Episode VII came out (right around my birthday, no less), my friends and I went to an all-day, big-screen marathon of the entire franchise, culminating in the debut of The Force Awakens–an experience I still consider one of the best days of my life. I care a lot about this franchise, is what I'm saying. So, naturally, I was as excited as anyone to see how it (or at least the Skywalker-centric portion of it) would end.
Unfortunately, the ending we got didn't live up to my hopes.
I should preface this review by saying a few things. First of all, I adore The Last Jedi. I know it's divisive among the fanbase, but I love that it tries to shake things up. I love how Rey and Kylo develop; I love the introduction of Rose; I even love Luke's self-imposed isolation, and his gradual realization that it was the wrong choice. I think the film took risks and was all the better for it.
Secondly, I'm a big fan of the new Expanded Universe. When Disney acquired the rights and announced they were purging the old EU (now branded Legends), I was excited. I had always been interested in the EU, but by the time I was old enough to get into it, it had already been going for decades. It felt too overwhelming to try to penetrate. A new EU, though? One I could jump into from the beginning? Heck yes, sign me up. I've loved the shows, comics, and books I've read in the Disney era.
More than anything else, it was these two things that led to my disappointment in Rise of Skywalker. It's a film that desperately does not want to be the sequel to The Last Jedi, on top of completely disregarding the EU. Oh, and to anyone who says (as I have frequently seen argued online) that director J. J. Abrams "had no choice" but to do the movie this way because "there was nowhere to go after Last Jedi," no. That's a completely ridiculous argument. At the end of TLJ, the Resistance needs rebuilt, Rey has the Jedi texts and could theoretically begin gathering and training new Jedi, Kylo had become completely unhinged, there could be unknown repercussions to Snoke's death… any one of those threads could've formed the basis of the next film.
Instead of exploring any of that, though, Rise of Skywalker takes an immediate wrong step by announcing in the opening crawl (yes, it's not even a decent on-screen reveal) that hey, Palpatine is back! Turns out he isn't dead (somehow) and he actually was Snoke, or was responsible for Snoke (somehow), and this whole trilogy has been part of his machinations all along (somehow). Don't get me wrong, I love Palpatine as a villain, but this is a terrible twist. It's executed poorly, it retroactively lessens the impact of Anakin's redemption and sacrifice (I guess he didn't kill Palpatine and restore balance after all, huh?), it's boring (turns out the bad guy is… the same old bad guy we've been dealing with for decades), and it completely messes up threads the EU has been setting up for years–without getting into it too much, the EU establishes the First Order as being an Imperial fragment that is specifically opposed to how Palpatine ran things, with further implications that there were beings in the Unknown Regions that even Palpatine feared who may have had a hand in the First Order's rise. But, uh… psych! It was all just an elaborate smokescreen by the Emperor, I guess.
The other frustrating thing about Palpatine's arrival is that Kylo Ren–whose whole development last film was summed up as "Leave the past behind; kill it, if you have to"–immediately teams up with him to establish a new Empire. Sure, we find out pretty quickly that he's trying to use Palpatine for his own ends, but… why? Seriously, the Emperor (and presumably his whole weird cult on Exegol) are Sith. The Sith actively encourage betrayal for personal gain. Why doesn't Kylo just kill the decrepit old Emperor and take his fancy fleet? The team-up seems completely unnecessary.
Ah, but it does set up one of my least favorite things about the film: the reveal that Rey is actually a Palpatine! Not only does this completely undermine TLJ's 'anyone can be a hero' theme, it's just… sort of lame. The hero secretly being related to the villain isn't unique or interesting–it's exactly what we saw in the original trilogy, actually. Sorry, kids–guess you can only be a big hero if your dad (or grandpa, in this case) was someone important!
Speaking of disappointing backstory reveals, what's the deal with Poe having been a spice runner? This is another one of those EU-breaking reveals that frustrates me to no end. We know from Shattered Empire that Poe's parents were Rebel pilots who served alongside Luke–that was all the explanation needed to justify his piloting skills and loyalty to Leia and the Resistance. What purpose does having him be a smuggler serve, except to make him marginally more similar to Han Solo? It doesn't add to the story in a meaningful way, it doesn't fit well with his established background, and it's foreshadowed very clumsily in the film itself–Finn keeps alluding to Poe being 'shady' because he… knew how to steal a speeder? Something a talented pilot/spy/high-ranking member of an underground resistance movement would probably know how to do regardless?
On the topic of Poe's weird new past, it's got some other baggage in the form of two new side-characters, Zorii Bliss and Babu Frik. I'm just gonna say it: Zorii Bliss is a waste of a character. She's got a cool character design and is played by a talented actress (Keri Russell), but her writing makes no sense. When Poe first shows up on her planet, she wants to kill him, and goes so far as to say, "All I want is to see your brains in the snow!" Then, about five minutes of screentime later, they're sharing a heartfelt conversation and she asks him to run away with her. What? She also implies early on that she doesn't care about the Resistance and resents that Poe left their gang to join it, but later makes a big deal about how evil the First Order is and how everyone needs to band together to stop them. It's the kind of development that would work over the course of the trilogy (or even a single film, if she had enough focus) but seems ludicrous in the tiny amount of screentime she gets. Babu Frik isn't quite as egregious (and is actually a fairly charming character on his own), except Zorii makes a big deal about the fact that "Babu Frik only works for the crew," only for him to end up having Resistance contacts on Endor later in the movie.
Let's talk about Endor, too, while we're at it. Endor actually has one of the better elements of Rise of Skywalker: Jannah and her crew of former Stormtroopers. The fact that there are other First Order deserters is cool, and I love the implication that Force sensitivity seems to be a factor in turning Stormtroopers against their masters. The fact that Finn is beginning to realize his connection to the Force is awesome, and something I had been hoping for all trilogy. Jannah seems neat as well, even if I do feel like she got to have all the scenes that should have gone to Rose–a character who was sidelined hard by this movie.
Sidebar: I thought Rose was great in TLJ, and to minimize her as this film did is a huge disservice to the character and her actress, Kelly Marie Tran, who suffered a torrent of online abuse after the last film; it is an act of capitulation to the worst part of the Star Wars fanbase and by far one of the poorest decisions in Rise of Skywalker.
Another bad choice? A large part of the movie revolves around hunting down a Sith wayfinder that will lead the characters to Palpatine's secret base. That's all fine and good, but it's handled so sloppily. The big macguffin for most of the plot is a knife that is inscribed with the wayfinder's location, which… what? Why? Who made this knife, and when? Why does it still work? It points to a specific spot in the ruins of the Death Star, which are in an ocean–surely the tides would have moved them or they would have eroded enough to shift or something by now! It doesn't make a lick of sense. Also, just to point out another wasted EU opportunity, there's a running thread in a few EU works about a mysterious compass Luke recovers from one of Palpatine's vaults post-Return of the Jedi; why couldn't that have been the key to finding Exegol? It would've paid off an interesting plot thread and tied into the larger universe!
Of course, this movie doesn't seem that interested in paying things off. Finn has something he needs to tell Rey? Eh, don't worry about it (if you're paying attention it's not too hard to infer that he wants to tell her about his Force sensitivity, but it's not hard to miss that, either). Knights of Ren finally show up? Eh, Ben takes them all out in a matter of minutes. Hux has turned traitor to take out Kylo? Whatever, just kill him off immediately so we don't have to deal with that. Threepio has to lose all his memories in a poignant sacrifice? Haha, nah, he gets them all back!
Hey, that's another thing about this movie: it loves to pretend to kill people. Chewie dies, except he doesn't; Threepio loses all his memories, except he doesn't; Rey fatally wounds Kylo, except then she heals him; Ben is killed by Palpatine, except then he's fine; Rey dies fighting Palpatine, except then Ben heals her. It's extremely frustrating, as is the fact that the only death that sticks is Ben's last death after saving Rey. Seriously? The trilogy goes to all this trouble to set up a redemption arc, he finally gives up being Kylo Ren, and then he gets a handful of scenes and no dialogue before unceremoniously fading away.
Also, it drives me nuts that he fades away at all! It's been established (again, in EU materials like The Clone Wars, though the implication is also in Revenge of the Sith) that becoming a Force ghost is an extremely difficult technique that only a handful of powerful Jedi have mastered. How does Ben know how to do it? More importantly, how does Leia–who gave up on her training pretty early (fun fact: she says in the novel Bloodline that she didn't train as a Jedi and never wanted to, but hey, apparently nothing in the EU matters)–manage to pull it off? And several hours after she died, too! Her corpse just disappears along with her son's, which… what?
Okay, okay, this has just turned into a rant at this point. Bottom line: this does not feel like a film that respects the franchise as a whole–not the EU, for sure, but not even earlier core films. Add to that the overall weak writing, storm of cliches (the big fleet arriving to save the day, the clumsy "Rey Skywalker" line at the end and its ham-fisted setup early on), and lack of engagement with its own characters (Poe and especially Finn have next to nothing to do until the very end), and you've got a pretty disappointing film.
That said, there are some bright spots. The action overall is good, with a special mention to the scene on the Star Destroyer above Kijimi–Poe and Finn's firefight in the halls is great, and the Force Bond duel between Rey and Kylo is astounding. In general, the Force Bond is used extremely well in this movie; it helps carry forward Rey and Kylo's relationship, and its ability to affect the physical world is one of the few decent payoffs we get.
The humor is also good, particularly the jokes involving C-3PO. I was pleasantly surprised to find the droid the funniest part of the film! Speaking of droids, the new droid D-O is adorable and I want to adopt him.
Leia was handled as well as could be expected (there was definitely some awkwardness, and her death was a bit anticlimactic, but given the circumstances that's completely understandable), and I loved the fact that she became Rey's master.
For the brief few scenes that we get him, Ben Solo is fantastic. Adam Driver completely changes from his Kylo persona, and it's wonderful to watch. It's a real shame that he dies before getting to develop further.
I have a lot more to say about this movie, but most of it is just more angry ranting. I just want to close by saying that, even though I really don't like this movie… that's okay. Do I wish it were different? Of course. Do I get kind of fired up about disliking it? Clearly, yes. But at the end of the day, it's just a movie. I don't wish ill on anyone involved in making it, and it didn't "ruin Star Wars." I've still got ten other great movies to watch (well… nine other great movies and The Phantom Menace). I've still got lots of EU stuff that I love. I've still got a ton of fond memories of the franchise, and I'm sure I'll make a ton more as new movies, games, and books come out.
…Okay, one last complaint, though: that blue lightsaber was NOT Luke's! It was Anakin's! Luke only used it until he learned how to build his own lightsaber! Luke's saber is the green one he used in Return of the Jedi!
Mobile Suit Gundam, Episode 8 – Winds of War
Mobile Suit Gundam, Episode 9 – Fly, Gundam!


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You've been seeing too much of the usual: stock images, the typical photo perspective that makes it to the postcard, a company's product gracefully posed on a table, and so on. It's time to take a different perspective.
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5 Photographers in Dahlonega, Georgia to Follow on Instagram
There are so many interesting small towns in the world and they happen to be full of talented people who are winning on social media. Therefore, let's focus on one small town and see who's producing amazing content. In this post, we're focusing on photographers in Dahlonega, Georgia who you might want to follow on Instagram. #dahlonega
Phil Procida
A photographer with a consistent aesthetic is Phil Procida, whose photos seem to have a dark sheen to them. I am mesmerized by his "glass ball" photo. Phil often features nature when the light is low, from dusk to even as late as 3am, so that he can play with an element of light in the composition. Some of his images will blow your lid off.
Cari Pilgrim
Cari Pilgrim calls herself a Dahlonega-based "elopement, wedding + portrait photographer." Her Instagram account is full of awesome wedding shots as well as details between shots, like photos of the landscape around her. You might also want to visit her website, where she does a bit of storytelling to provide context to the photos. One of my favorite photos by this skilled wedding photographer is what I call "the leaning tree."
Michelle + Brandon Horn
Meet the dynamic duo Michelle and Brandon Horn, who are husband and wife photographers in Dahlonega who describe themselves in three words: adventure, love, and life. They are great at creating not only a consistent visual aesthetic, but also a consistent value-based focus to their posts. For example, they include faithful messages in their Instagram feed, like their post about why they love to honor the Sabbath Day. Their photos capture the remarkable, intimate moments of weddings, such as their shot of a bride dabbing a tear from her eye.
Wisdom&Stature
Wisdom&Stature Photography is owned by Rachel, a "traveling wedding + portrait photographer" in Dahlonega who is "dedicated to capturing the beauty of every single moment." Her Instagram feed has a professional feel, with each photo capturing that perfect moment. She'll even make your shoes look good.
Brittany Peters
On the other end of the spectrum is Brittany Peters of Nine Sixteen Photography, whose Instagram feed makes you feel like you're family. She shares the personal moments of her daily experience that help a prospective client understand the type of person he or she will be working with, because we know it's not just about talent—it's about personality too! She specializes in adorable Anne Geddes-style baby photos, and true to her personality, she throws in a few not-so-baby baby pics.
Who is your favorite Dahlonega-based photographer?
If you know of other talented photographers in Dahlonega and have a favorite Instagram account to share, please comment on this post! Let's help give some visibility to those skilled with the camera in Northern Georgia.
Tagged: photography, Instagram, content
Forget Instagram Pods; Join a Shop Share Group
Instagram strategies are on the rise: meet Shop Share Groups, which Instagrammers are saying is better for business than Instagram pods.
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Categorizing Content and Defining the Word "Similar"
What does it mean to find a "similar" tweet or photograph? What does the word similar really mean for social media?
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Technology Attorney Andrew S Bosin LLC helps clients develop, protect and monetize their intellectual property and provides legal advice to SaaS, Software, Cloud, Mobile App and E-Commerce startups, vendors, providers, developers, licensees, entrepreneurs and enterprise companies. Located in New Jersey just outside of New York City Andrew helps entrepreneurs all across the United States.
Andrew is one of the very few lawyers out there that has the rare combination of both legal and entrepreneurial experience to best serve your growing legal needs whether you are just starting out or about to sell your business.
On the entrepreneur side Andrew has owned a SaaS company with partners having built the business from a drawing on a napkin to being issued a patent. Andrew's startup experience has been priceless. At the same time he has been counseling clients Andrew has been involved in just about every business and legal decision that has been made in his own startup from inception, development of the software, sales, marketing and putting together global sales deals. Because of this experience, Andrew can help you avoid making the same mistakes that startups all too often make while trying to scale.
Andrew also give clients the best possible personal service not offered by most other attorneys. How so? Andrew gives clients access to him night and day and weekends via his cell number. Unlike most other lawyers, Andrew encourages and wants his clients to call or text him whether its during the work day or at night and on weekends.
Andrew also offers affordable legal advice for a flat rate fixed legal fee so his clients know the exact amount in fees they are being charged. Andrew does not engage in games like some firms that charge clients large retainers and then send an even larger bill at the end of the month. For a startup this type of legal billing can cause chaos and disruption to business operations.
Andrew drafts and negotiates SaaS Agreements, SaaS Contracts, SaaS Subscription Agreements, SaaS Vendor and Service Provider Contracts and Agreements, SaaS Value-Added Re-Seller Agreements, Saas Licensing Agreements, Software License Agreements, Software Licensing Agreements, technology transactions, technology agreements, technology license agreements, technology licensing agreements, technology contracts, End User Licensing Agreements (EULAs), Software Contracts, App Contracts, Mobile App Agreements, Mobile App Development Contracts, Mobile Application Licensing Agreements, Software Application Development Agreements & Video Game App Contracts.
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Companies engaging in tech, software or SaaS deals with enterprise customers for the first time better get plenty of sleep and rest before they start negotiating the Agreement. With plenty of money to spend on in-house lawyers who know every issue and are use to getting things their way a smaller technology or software vendor often looks and feels like a deer in the headlights.
For more small technology companies there is not too much resistance in contracts negotiations from other smaller companies who subscribe to or license their products. Non-enterprise customers simply don't have the funds to hire a lawyer to spend hours eyeballing the vendor's agreement. More often than not, in this type of negotiation there are no lawyers involved in the deal.
For smaller companies dealing for the first time with an enterprise customer you need to be prepared for major contracts negotiations by the customer. Especially the language dealing with limitations of liability and warranties which the enterprise customer will completely re-write so the vendor assumes much more liability and offers more in the way of liability.
So what can a technology or software company do to prepare to negotiate an agreement with an enterprise customer? For starters, you need to purchase a sufficient amount of general liability and errors and omissions insurance. You will not sign any contracts with any enterprise customers without getting a decent amount of commercial insurance. Enterprise companies want to know that in the event of a data breach or cyber attack that a vendor has a sufficient amount of insurance to cover any losses or damages.
Next, you need to get your security measures and protocols in place to protect customer data and sensitive information. There is so much information on the web about this issue plus you can hire a company that specializes in performing security assessments or that certifies tech and software companies that their security measures are acceptable or up to industry standards.
You will also need to be prepared to accept liability if you outsource hosting and serving of your product to a third party cloud managed services provider. Enterprise companies don't care that your cloud provider is one of the largest companies in the world. If something goes wrong with the service and the customer's data is compromised or destroyed they can't sue your cloud provider so they are going to look to your company to assume such liability.
You also need to know that if you are offering a SaaS application that the enterprise customer might want you to do some customized work to your software so that it integrates better with the customer's platform or brands. This raises the issues of cost and timing. Is the customer going to pay for all of this customized work? And, will this delay the launch of the customer using your software live?


:: |bTc| the.fun.clan :: • View topic - "id Software have turned off their master server"
"id Software have turned off their master server"
Post subject: "id Software have turned off their master server"
As all ET Players have probably figured out by now, id Software have turned off their master server for unknown reasons. This basically means that there are no server listings available at all for the time being. Nobody knows when they will turn it back on, if at all.
There are two ways to fix it.
2. Installing the fixed "et.exe" or a "service tool"
Post subject: Re: "id Software have turned off their master server"
Um die (alte) Liste wiederherzustellen, müßt ihr wieder die originale ET.exe tauschen oder den Windows-Hosts-Eintrag entfernen.
Leider ist der RtCW Masterserver immer noch ausgeschaltet.


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text:
The Stan Simpson Show: Ex-inmate tries to make things right
HARTFORD – Stan talks with a former prison inmate who is seeking to transform drug dealers into legitimate entrepreneurs. Bashaun Brown is a convicted ban...
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Author: FOX61 Staff
Published: 5:30 AM EDT May 21, 2016
Updated: 5:30 AM EDT May 21, 2016
HARTFORD - Stan talks with a former prison inmate who is seeking to transform drug dealers into legitimate entrepreneurs. Bashaun Brown is a convicted bank robber, and now he is trying to make things right with a venture that will teach entrepreneurial skills to drug dealers so that they can conduct their business legally.
Brown served six years for bank robbery, is working to establish a venue for an upcoming event in the co-working space reSET, among other things. reSET serves entrepreneurs and aims to inspire innovation and community collaboration and they have been mentoring Bashaun in his business venture.
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