  Deep convolutional neural networks (CNNs) for image denoising can effectively
exploit rich hierarchical features and have achieved great success. However,
many deep CNN-based denoising models equally utilize the hierarchical features
of noisy images without paying attention to the more important and useful
features, leading to relatively low performance. To address the issue, we
design a new Two-stage Progressive Residual Dense Attention Network
(TSP-RDANet) for image denoising, which divides the whole process of denoising
into two sub-tasks to remove noise progressively. Two different attention
mechanism-based denoising networks are designed for the two sequential
sub-tasks: the residual dense attention module (RDAM) is designed for the first
stage, and the hybrid dilated residual dense attention module (HDRDAM) is
proposed for the second stage. The proposed attention modules are able to learn
appropriate local features through dense connection between different
convolutional layers, and the irrelevant features can also be suppressed. The
two sub-networks are then connected by a long skip connection to retain the
shallow feature to enhance the denoising performance. The experiments on seven
benchmark datasets have verified that compared with many state-of-the-art
methods, the proposed TSP-RDANet can obtain favorable results both on synthetic
and real noisy image denoising. The code of our TSP-RDANet is available at
https://github.com/WenCongWu/TSP-RDANet.
