# Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior

arXiv: https://arxiv.org/abs/1707.05414 · HTML: https://mlsanity.com/paper/1707.05414/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images](https://mlsanity.com/paper/2101.09870/) (2021)
- [A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques](https://mlsanity.com/paper/2301.04292/) (2023)

## Abstract (arXiv)

We explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn similar pixel-distribution features from noisy images. Many types of image noise follow a certain pixel-distribution in common, such as additive white Gaussian noise (AWGN). By increasing CNN's width with larger reception fields and more channels in each layer, CNNs can reveal the ability to extract more accurate pixel-distribution features. The key to our approach is a discovery that wider CNNs with more convolutions tend to learn the similar pixel-distribution features, which reveals a new strategy to solve low-level vision problems effectively that the inference mapping primarily relies on the priors behind the noise property instead of deeper CNNs with more stacked nonlinear layers. We evaluate our work, Wide inference Networks (WIN), on AWGN and demonstrate that by learning pixel-distribution features from 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. textitCode and models are available at urlhttps://github.com/cswin/WIN.