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Research on blurred image restoration based on generative adversarial networks
Author(s): Yan Wan; Jinghua Fan; Min Liu; Yingbin Zhao; Jianpeng Jiang; Li Yao
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Paper Abstract

In order to realize the restoration of the blurred image by using the Generative Adversarial Networks, this paper proposes to add generator loss optimization and network depth optimization based on the generation of the Generative Adversarial Networks(GANs) with gradient penalty. This paper adds Perceptual Loss and ResNet. The perceptual loss is migrated from the image style migration network module as the second item added to the loss to the generator loss function, learning the clear image style and facilitating the correction generation. Add residual modules to the generator network to reduce network degradation while deepening network depth. The network structure model optimized in this paper shows relatively good test results in the subsequent experiments.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790M (14 August 2019); doi: 10.1117/12.2540315
Show Author Affiliations
Yan Wan, Donghua Univ. (China)
Jinghua Fan, Donghua Univ. (China)
Min Liu, Donghua Univ. (China)
Yingbin Zhao, Donghua Univ. (China)
Jianpeng Jiang, Donghua Univ. (China)
Li Yao, Donghua Univ. (Chile)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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