Super-resolution algorithm using an improved generative adversarial network

Yikai Gu; Weiqin Huang

doi:10.1117/12.2639522

19 May 2022 Super-resolution algorithm using an improved generative adversarial network

Yikai Gu, Weiqin Huang

Proceedings Volume 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022); 122500I (2022) https://doi.org/10.1117/12.2639522
Event: International Symposium on Computer Applications and Information Systems (ISCAIS2022), 2022, Shenzhen, China

Abstract

SRGAN applies generative adversarial networks to super-resolution reconstruction, and the advent of this algorithm greatly facilitates image reconstruction based on deep learning. It introduces batch normalization for stable training, and although batch normalization can facilitate training, it destroys the original contrast information of the image and affects the quality of image reconstruction. For this problem, this paper proposes an super-resolution algorithm based on improved SRGAN, which removes the batch normalization in the residual block of the generative network to avoid its bad influence on the reconstructed image and improve the quality of the reconstructed image for better. In addition, the total variation is introduced in the loss function of the discriminative model to constrain the gradient change, which serves to stabilize the network training and accelerate the convergence. Experiments of the proposed super-resolution algorithm on a large number of public datasets show that this method achieves better results under various evaluation metrics compared with other similar algorithms.

Citation Download Citation

Yikai Gu and Weiqin Huang "Super-resolution algorithm using an improved generative adversarial network", Proc. SPIE 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022), 122500I (19 May 2022); https://doi.org/10.1117/12.2639522

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KEYWORDS

Reconstruction algorithms

Image quality

Super resolution

Lawrencium

Gallium nitride

Image restoration

Feature extraction

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