Fix noisy images with two stages to learn good features and suppress bad features with TSP-RDANet
Fix noisy images with two stages to learn good features and suppress bad features with TSP-RDANet
Two-stage Progressive Residual Dense Attention Network for Image Denoising
arXiv paper abstract https://arxiv.org/abs/2401.02831
arXiv PDF paper https://arxiv.org/pdf/2401.02831.pdf
Deep convolutional neural networks (CNNs) for image denoising can ... exploit ... hierarchical features ... However, many ... equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance.
... design a new Two-stage Progressive Residual Dense Attention Network (TSP-RDANet) ... to remove noise progressively.
Two different attention mechanism-based denoising networks ... 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.
... compared with many state-of-the-art methods, the proposed TSP-RDANet can obtain favorable results both on synthetic and real noisy image denoising ...
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