Optimization of BP Neural Network Image Restoration based on Improved Cuckoo Algorithm

Abstract

To address the limitations of BP neural networks, such as their sensitivity to initial weights and thresholds and their tendency to get stuck in local optima, this paper uses the cuckoo algorithm to search for the initial weights and thresholds of BP neural networks, thereby eliminating their dependence on initial weights and thresholds. Improvements were made to the step size and discovery probability of the cuckoo algorithm. The improved cuckoo algorithm was used to search for the initial weights and thresholds of the BP neural network, and the optimized network was applied to restore the Lena image . Experimental results show that for the Lena image, the ICS-BP neural network achieves a peak signal-to-noise ratio (PSNR) that is 4.83% and 3.53% higher than the BP neural network and PSO-BP neural network, respectively, and a structural similarity (SSIM) that is 1.62% and 0.67% higher than the BP neural network and PSO-BP neural network, respectively, indicating that the optimized BP neural network achieves better image restoration performance. Additionally, the ICS-BP converged after 35 iterations, which is faster than the BP neural network (137 iterations) and the PSO-BP neural network (81 iterations), thereby improving efficiency.