Dual residual attention network for image denoising

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) that is generated during image acquisition or transmission, which severely impedes their application in practical image denoising tasks. In this paper, we propose a novel Dual-branch Residual Attention Network (DRANet) for image denoising, which has both the merits of a wide model architecture and the attention-guided feature learning. The proposed DRANet includes two different parallel branches, which can capture complementary features to enhance the learning ability of the model. We designed a new residual attention block (RAB) and a novel hybrid dilated residual attention block (HDRAB) for the upper and lower branches, respectively. The RAB and HDRAB can capture rich local features through multiple skip connections between different convolutional layers, and the unimportant features can be dropped. Meanwhile, the long skip connections in each branch and the global feature fusion between the two parallel branches can effectively capture the global features as well. Extensive experiments demonstrate that compared with other state-of-the-art denoising methods, our DRANet can produce competitive denoising performance both on the synthetic and real-world noise removal. The code for DRANet is accessible at https://github.com/WenCongWu/DRANet.