Learning a Multi-scale Deep Residual Network of Dilated-Convolution for Image Denoising

Abstract

Due to the favorable denoising performance of the discriminative learning model, a large number of discriminative learning models have been designed to remove noise. However, most discriminative learning models can only deal with the noise that already exists in the training data. In this paper, a multi-scale trainable deep residual convolutional neural network (DCMSNet) based on dilated convolution is proposed. DCMSNet consists of a chain of dilated convolution layers, convolution layers, normalized multi-scale convolution blocks (BNMCBlock), multi-scale convolution blocks (MCBlock) and multi-scale dropout convolution blocks (MCDBlock). The use of dilated convolution can avoid over-parameterization problems caused by too deep networks. In order to capture more feature information during feature extraction and image reconstruction, we designed novel BNMCBlock and MCBlock. In order to reduce the degree of coupling between image features and improve the generalization ability of the network, we also designed a MCDBlock. Meanwhile, residual learning, batch normalization and dropout are utilized to speed up the training process and boost the denoising performance. Unlike existing denoising models, DCMSNet is able to remove different degrees of noise. Compared with state-of-the-art image denoising methods, DCMSNet has achieved relatively competitive results.