Error feedback denoising network - Ruizhi Hou; Fang Li (IET Image Processing, 2021)
Abstract Recently, deep convolutional neural networks have been successfully used for image denoising due to their favourable performance. This paper examines the error feedback mechanism to image denoising and propose an error feedback denoising network. Specifically, we use the down‐and‐up projection sequence to estimate the noise feature. By the residual connection, the clean structures are removed from the noise features. The essential difference between the proposed network and other existing feedback networks is the projection sequence. Our error feedback projection sequence is down‐and‐up, which is more suitable for image denoising than the existing up‐and‐down order. Moreover, we design a compression block to improve the expression ability of the general 1×1 convolutional compression layer. The advantage of our well‐designed down‐and‐up block is that the network parameters are fewer than other feedback networks and the receptive field is enlarged. We have implemented our error feedback denoising network on denoising and JPEG image deblocking. Extensive experiments verify the effectiveness of our down‐and‐up block and demonstrate that our error feedback denoising network is comparable with the state‐of‐the‐art. The code will be open source. The source codes for reproducing the results can be found at: https://github.com/Houruizhi/EFDN.