Murals Super-resolution Reconstruction with the Stable Enhanced Generative Adversarial Network

Authors

Jianfang Cao, 1.College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;2.Department of Computer, Xinzhou Teachers University, Xinzhou 034000, China;Follow
Yiming Jia, 1.College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
Minmin Yan, 1.College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
Xiaodong Tian, 1.College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;

Abstract

Abstract: Aiming at the problems of low resolution and unclear texture details of ancient murals, which led to insufficient viewing of murals and low research value, a stable enhanced super-resolution generative adversarial networks (SESRGAN) reconstruction algorithm is proposed. Based on the generative adversarial network, the generative network uses dense residual blocks to extract mural features, and uses the visual geometry group (VGG) network as the basic framework of the discriminating network to determine the authenticity of the input mural, and introduces perception loss, content loss and penalty loss to jointly optimize the model. Experimental results show that, compared with other related super-resolution algorithms, the peak signal-to-noise ratio (PSNR) is improved by 0.4~2.62 dB on average, the structural similarity is improved by 0.013~0.027, and the subjective perception evaluation is also improved.

Recommended Citation

Cao, Jianfang; Jia, Yiming; Yan, Minmin; and Tian, Xiaodong (2022) "Murals Super-resolution Reconstruction with the Stable Enhanced Generative Adversarial Network," Journal of System Simulation: Vol. 34: Iss. 5, Article 14.
DOI: 10.16182/j.issn1004731x.joss.20-0989
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss5/14

First Page

1076

Revised Date

2021-03-10

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0989

Last Page

1089

CLC

TP391.47

Recommended Citation

Jianfang Cao, Yiming Jia, Minmin Yan, Xiaodong Tian. Murals Super-resolution Reconstruction with the Stable Enhanced Generative Adversarial Network[J]. Journal of System Simulation, 2022, 34(5): 1076-1089.

DOI

10.16182/j.issn1004731x.joss.20-0989