Selective Residual M-Net for Real Image Denoising

• URL: http://arxiv.org/abs/2203.01645v1
• Date: Thu, 3 Mar 2022 11:10:30 GMT
• Title: Selective Residual M-Net for Real Image Denoising
• Authors: Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu
• Abstract summary: We propose a blind real image denoising network (SRMNet) to advance the performance of denoising algorithms. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. OurNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality.
• Score: 6.909688694501238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration is a low-level vision task which is to restore degraded images to noise-free images. With the success of deep neural networks, the convolutional neural networks surpass the traditional restoration methods and become the mainstream in the computer vision area. To advance the performanceof denoising algorithms, we propose a blind real image denoising network (SRMNet) by employing a hierarchical architecture improved from U-Net. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/TentativeGitHub/SRMNet.

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