Research on blurred image restoration based on generative adversarial networks

Yan Wan; Jinghua Fan; Min Liu; Yingbin Zhao; Jianpeng Jiang; Li Yao

doi:10.1117/12.2540315

14 August 2019 Research on blurred image restoration based on generative adversarial networks

Yan Wan, Jinghua Fan, Min Liu, Yingbin Zhao, Jianpeng Jiang, Li Yao

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790M (2019) https://doi.org/10.1117/12.2540315
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Yan Wan, Jinghua Fan, Min Liu, Yingbin Zhao, Jianpeng Jiang, and Li Yao "Research on blurred image restoration based on generative adversarial networks", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790M (14 August 2019); https://doi.org/10.1117/12.2540315

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available