Can a Single Image Denoising Neural Network Handle All Levels of Gaussian Noise?

Abstract

A recently introduced set of deep neural networks designed for the image denoising task achieves state-of-the-art performance. However, they are specialized networks in that each of them can handle just one noise level fixed in their respective training process. In this letter, by investigating the distribution invariance of the natural image patches with respect to linear transforms, we show how to make a single existing deep neural network work well across all levels of Gaussian noise, thereby allowing to significantly reduce the training time for a general-purpose neural network powered denoising algorithm.