Residual Networks with Channel Attention for Single Image Super-Resolution
Authors: 
Yadong Wang, Junmin Wu, Hui WangAuthors Info & Claims
Yadong Wang, Junmin Wu, Hui WangAuthors Info & ClaimsArticle No.: 105, Pages 1 - 5
Abstract
With the development of convolution neural network (CNN), CNN-based methods also achieve a great success for image super-resolution tasks. Further, ResNet makes it possible that the network of super-resolution can be trained deeply. However, simply increasing the depth or width of the network has a scant improvement on the reconstruction quality. Therefore, we need to exploit some new mechanisms to boost the quality of the reconstructed SR images. In this paper, we utilize channel attention mechanism to rescale channel-wise features and extract the desired high-frequency information. Additionally, we add feature fusion structure into the network in order to make full use of all the extracted middle high-frequency information instead of that extracted only by the final layer. Experiments we have conducted show that the network we proposed could reconstruct high quality images with only a few parameters.
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
Copyright © 2021 ACM.
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Published: 25 February 2022
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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