Open AccessMulti-scale residual network-based image restoration
Author(s) -
Lei Yang,
Fei You
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2189/1/012009
Subject(s) - deblurring , residual , image restoration , artificial intelligence , computer science , convolutional neural network , image (mathematics) , scale (ratio) , computer vision , degradation (telecommunications) , artificial neural network , pattern recognition (psychology) , image processing , algorithm , geography , cartography , telecommunications
All Image restoration technology has undergone extensive research. For image degradation, inverse filtering was proposed in the last century. Then Helstrom proposed a Wiener filtering algorithm. In recent years, the convolutional neural network has promoted the further development of image restoration technology. The coarse-to-fine multi-scale residual network Multiscale Deblur Net (MDN) used in this paper is stable in operation, simple in structure, and easy to train. It has a good deblurring ability for motion-blurred images by testing in the GO_PRO dataset.