Two-Stage Filter Response Normalization Network for Real Image Denoising

Abstract

In this paper, we propose a two-stage network for real image denoising with filter response normalization, named as two-stage filter response normalization network (TFRNet). In TFRNet, we propose a filter response normalization(FRN) block to extract features and accelerate the training of the network. TFRNet consists of two stages, at each stage of which we use the encoder-decoder structure based on U-Net. We also use the coordinate attention block(CAB), double channel downsampling module, double skip connection module, and convolutional (Conv) block in our TFRNet. With the help of these modules, TFRNet provides excellent results on both SIDD and DND datasets for real image denoising.