A super-resolution image reconstruction method based on generative adversarial network

Xiaohui Zhou; Li Gao

doi:10.1117/12.2659326

30 November 2022 A super-resolution image reconstruction method based on generative adversarial network

Xiaohui Zhou, Li Gao

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124562M (2022) https://doi.org/10.1117/12.2659326
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

Compared with traditional methods, the quality of reconstructed images has been greatly improved by super-resolution image reconstruction methods based on deep residual networks. However, in order to make full use of shallow image features and solve the problem of instability during network training. This paper proposes a method for image super-resolution reconstruction that can better reconstruct texture details based on generative adversarial network (GAN). Dense blocks containing residual scaling (RRDBs) is used to construct the generative network to extract more image features. WGAN is introduced to construct the discriminative network to solve the problem of unstable training of generative adversarial network. The experimental results show that the proposed model in this paper has better visual effects and improved SSIM and PSNR values compared with other models.

Citation Download Citation

Xiaohui Zhou and Li Gao "A super-resolution image reconstruction method based on generative adversarial network", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124562M (30 November 2022); https://doi.org/10.1117/12.2659326

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KEYWORDS

Reconstruction algorithms

Super resolution

Image restoration

Image enhancement

Image processing

Convolution

Evolutionary algorithms

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