Super-Resolution Reconstruction Algorithm Based on Improved Generative Adversarial Network
Authors: 
Xiangyu Deng, Yao Ma, Yangyang BianAuthors Info & Claims
Xiangyu Deng, Yao Ma, Yangyang BianAuthors Info & ClaimsPages 1816 - 1821
Abstract
The current super-resolution algorithm for generative adversarial networks (SRGAN) has problems such as an unstable model training process and excessive smoothing of reconstructed images, which can affect the quality of generated images to a large extent. In this paper, based on SRGAN, all BN layers in the generative network are removed, and WGAN is used instead of JS scatter to optimize the discriminate network, This efficiently prevents the phenomenon of gradient disappearance and resolves the issue of unstable training of generative adversarial networks. The SA module is added to the vgg19 feature extraction network to obtain better feature information and improve the quality of the generated images. The experiments show that the proposed method has better stability in the training process compared with the traditional SRGAN on the DIV2K datasets, improvements are made to the reconstructed images' peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual effect performance.
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Published In
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
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Association for Computing Machinery
New York, NY, United States
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Published: 15 March 2023
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EITCE 2022
EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 21 - 23, 2022
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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