Denoising convolutional neural network inspired via multi-layer convolutional sparse coding

Abstract

Sparse prior to image denoising is a classical research field with a long history in computer vision. We propose an end-to-end supervised neural network, named DnMLCSC-net, which is inspired via multi-layer convolutional sparse coding model embedded with symbiotic analysis–synthesis priors for natural image denoising. Unfolding a multi-layer, learned iterative soft thresholding algorithm (ML-LISTA) and developing into a convolutional recurrent neural network, all parameters in the model are updated adaptively to minimize mixed loss via gradient descent using backpropagation. In addition, a combined ReLU function is taken as the activation function. Inconsistent dilated convolution and batch normalization were empirically introduced into the encoding layers corresponding to the first iteration of ML-LISTA. Experimental results show that our network achieves a competitive denoising effect in comparison with several state-of-the-art denoising methods.