Optimizing BP Neural Network Image Restoration Based on an Improved Particle Swarm Optimization Algorithm

Ke Fang; Jianhong Li

doi:10.54097/ev7fwn34

Authors

Ke Fang
Jianhong Li

DOI:

https://doi.org/10.54097/ev7fwn34

Keywords:

Image Restoration, Intelligent Optimization Algorithm, BP Neural Network

Abstract

To address the shortcomings of BP neural networks, such as sensitivity to initial weights and thresholds and susceptibility to local optima, this paper proposes a particle swarm optimization (PSO) algorithm to optimize the BP neural network image restoration model. By searching for the initial weights and thresholds of the BP neural network, the dependence on these parameters is eliminated. The particle swarm optimization algorithm is modified, and the improved algorithm is used to search for the initial weights and thresholds of the BP neural network. The optimized network is then applied to restore the Cameraman image. Experimental results demonstrate that for the Cameraman image, the PSO-BP neural network achieves a peak signal-to-noise ratio (PSNR) improvement of 18.07% and 1.25%, respectively, compared to the standard BP neural network. Additionally, the structural similarity (SSIM) improves by 4.18% and 0.95%, respectively. These findings indicate that the optimized BP neural network delivers superior image restoration performance.

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