Dilated Residual Networks with Symmetric Skip Connection for image denoising

Due to the fast inference and good performance, convolutional neural network (CNN) has been widely applied in image denoising. Some new approaches, such as residual learning and batch normalization are quite effective at accelerating the training process as well as improving accuracy. The batch normalization has been proved to handle Gaussian denoising effectively and efficiently, and can further boost the denoising performance of networks. However, it still leaves much space to improve. In this paper, we attempt to introduce a novel network structure without batch normalization, namely Dilated Residual Networks with Symmetric Skip Connection (DSNet), which depends on a combination of symmetric skip connection and dilated convolution. The advantage of this novel approach is computationally efficient in training, because the layers and the parameters of our networks are less than the previous network structure. Our network structure is more feasible for the task of image denoising, especially Gaussian noise. Our extensive experiments demonstrate that the proposed method can not only outperform the state-of-the-art methods in terms of both accuracy and speed, but also be efficiently implemented by benefiting from GPU computing. (C) 2019 Elsevier B.V. All rights reserved.