Open AccessCombination of multi‐scale and residual learning in deep CNN for image denoising
Author(s) -
Xia Haiying,
Zhu Fuyu,
Li Haisheng,
Song Shuxiang,
Mou Xiangwei
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1386
Subject(s) - residual , noise reduction , computer science , artificial intelligence , noise (video) , image denoising , convolution (computer science) , pattern recognition (psychology) , image (mathematics) , scale (ratio) , cascade , layer (electronics) , algorithm , artificial neural network , chemistry , physics , organic chemistry , quantum mechanics , chromatography
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.