Combination of multi‐scale and residual learning in deep CNN for image denoising. Issue 10 (7th July 2020)

• Record Type: Journal Article
• Authors: Xia, Haiying; Zhu, Fuyu; Li, Haisheng; Song, Shuxiang; Mou, Xiangwei
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2013
• Page End: 2019
• Publication Date: 2020-07-07
• Subjects: learning (artificial intelligence) -- image denoising -- feature extraction -- convolutional neural nets; clean image -- noise observation -- high noise levels -- image denoising network -- residual learning -- multiscale schemes -- multilayer convolutions -- dilated convolutions -- common convolutions -- residual blocks; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1386
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : To better restore a clean image from a noise observation under high noise levels, the authors propose an image denoising network based on the combination of multi‐scale and residual learning. Instead of using filters with different large sizes in traditional multi‐scale schemes, they arrange multi‐layer convolutions with the filters of the same size to speed up the model. Some dilated convolutions of different rates are combined with the common convolutions to enrich the extracted features in multi‐layer convolutions. Furthermore, they cascade the multi‐layer convolutions with residual blocks to improve the performance of image denoising. Their extensive evaluations on several challenging datasets demonstrate that the proposed model outperforms the state‐of‐art methods under all different noise levels in terms of peak signal‐to‐noise ratio, and the visual effects achieved by the proposed model are also better than the competing methods.