Multi-scale Network Toward Real-World Image Denoising.
International journal of machine learning and cybernetics(2022)
摘要
Images are inevitably degraded when captured due to the effects of noise, and thus denoising is required. Previous methods remove real-world noise, while also causing issues with over-smoothing image details and loss of edge information. To solve these issues, a multi-scale image denoising network (MSIDNet) is proposed in this paper. We design a residual attention block (RAB) to encode and decode the context well, while introducing a selective kernel feature fusion module to fuse multi-scale features and obtain rich contextual information from low-resolutions to restore more details. A feature extraction block (FEB) is designed to fully extract local and global features then fusion, which obtains rich feature information. Extensive experiments on four real-world image datasets demonstrate that our method has excellent generalization and achieves advanced denoising performance on both peak signal-to-noise ratio and structural similarity. MSIDNet preserves more edge details and improves the over-smoothing issue to enhance the visual effect of denoised images.
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关键词
Image denoising,Real-world,Multi-scale,Feature extraction,Residual learning
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