Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
This addresses image denoising for computer vision applications, presenting a novel approach that improves over existing methods.
The paper tackles image denoising by using wider convolutional neural networks to learn pixel-distribution priors from noisy data, achieving significantly better performance than state-of-the-art deep CNN methods on additive white Gaussian noise in quantitative and visual evaluations.
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}}.