  In this work, we explore an innovative strategy for image denoising by using
convolutional neural networks (CNN) to learn pixel-distribution from noisy
data. By increasing CNN's width with large reception fields and more channels
in each layer, CNNs can reveal the ability to learn pixel-distribution, which
is a prior existing in many different types of noise. The key to our approach
is a discovery that wider CNNs tends to learn the pixel-distribution features,
which provides the probability of that inference-mapping primarily relies on
the priors instead of deeper CNNs with more stacked nonlinear layers. We
evaluate our work: Wide inference Networks (WIN) on additive white Gaussian
noise (AWGN) and demonstrate that by learning the pixel-distribution in images,
WIN-based network consistently achieves significantly better performance than
current state-of-the-art deep CNN-based methods in both quantitative and visual
evaluations. \textit{Code and models are available at
\url{https://github.com/cswin/WIN}}.
