Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Abstract

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise (AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking.

Existing System

Sparse Models, Gradient Models, Trainable Nonlinear Reaction Diffusion (TNRD) Model, Cascade of Shrinkage Fields (CSF) Method, Markov Random Field (MRF) Models.

Proposed System

In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. We propose an end-to-end trainable deep CNN for Gaussian denoising. In contrast to the existing deep neural network-based methods which directly estimate the latent clean image, the network adopts the residual learning strategy to remove the latent clean image from noisy observation. We find that residual learning and batch normalization can greatly benefit the CNN learning as they can not only speed up the training but also boost the denoising performance. For Gaussian denoising with a certain noise level, DnCNN outperforms state-of-the-art methods in terms of both quantitative metrics and visual quality. Our DnCNN can be easily extended to handle general image denoising tasks. We can train a single DnCNN model for blind Gaussian denoising, and achieve better performance than the competing methods trained for a specific noise level. Moreover, it is promising to solve three general image denoising tasks, i.e., blind Gaussian denoising, SISR, and JPEG deblocking, with only a single DnCNN model.

Architecture

BLOCK DIAGRAM