Title: Research on sports image restoration algorithm based on deep learning

Authors: Xinjiao Zhang; Xuefei Tao

Addresses: Department of Physical Education and Research, Shangluo University, Shangluo, 726000, China ' Physical Education Institute, Anqing Normal University, Anqing, 246133, China

Abstract: To improve the sharpness and realism of sports image restoration, an image restoration method based on deep learning is proposed. The improved generative adversarial network (GAN) is adopted for image denoising, and the improved RAiA-Net is used for image rain removal. The simulation results show that the peak signal-to-noise ratio and structural similarity of the improved GAN network reach 32.56 and 0.91, respectively, which are 3.45 and 0.04 higher than those of GAN network. The peak signal-to-noise ratio and structural similarity of RAiA-Net are 36.87 and 0.98 respectively, and the time consumption is shorter (0.43 s), which has certain advantages and practicability. Compared with the standard GAN network, CBM3D network and CNN network, the designed network has better image denoising performance and better performance in peak signal-to-noise ratio and structural similarity indexes.

Keywords: deep learning; image restoration; image denoising; image rain removal; generative adversarial network; GAN.

DOI: 10.1504/IJBIC.2026.155132

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.4, pp.246 - 257

Received: 29 Dec 2023
Accepted: 22 Oct 2024
Published online: 28 Jul 2026 *

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