改进的Hopfield网络图像复原

基于对图像恢复Paik的Hopfield网络模型的分析，对图像复原提出了一种改进的基于连续函数的全并行自反馈Hopfield网络，通过引入计算的参数γ，而不是按照实验或经验获得，可以使网络收敛速度更快。改进后的Hopfield网络模型对退化图像的复原结果与J.K. Paik的方法比较，结果表明此算法使图像复原处理更快，并且图像恢复效果好。与固定参数γ比较显示，计算出的参数γ对网络有更好的收敛速度。

Improved Hopfield Network for Image Restoration

Based on the analysis of Paik’s Hopfield network for image restoration, this paper presents a modified full parallel self-feedback continuous Hopfield network model to restore degraded image. And by introducing a calculated parameter γ rather than γ obtained by experiments or experience, the modified network converge can be made faster . The result of the model in restoring blurred image is compared with that of J.K. Paik’s method, which shows the method makes the restoration process faster and output better restoration quality. In experiment 2, compared with the constant parameter, it testifies that the calculated parameter γ can give better convergence rate for the network.