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
this https URL

设计了一种新的两阶段渐进残差密集注意力网络（TSP-RDANet）进行图像去噪，通过两个子任务逐步去除噪声，其中使用残差密集注意力模块（RDAM）和混合扩张残差密集注意力模块（HDRDAM）来设计两个基于注意力机制的去噪网络，以增强去噪效果。实验证实，与众多最新方法相比，所提出的 TSP-RDANet 在合成和真实噪声图像去噪方面取得了有利的结果。