基于改进Paik型Hoptield网络的图像复原

针对图像复原提出了一种改进的Paik型Hopfield网络神经元状态变化规则,在此基础上详细讨论了全并行算法的收敛性、残值误差和能量变化,并依据"由粗至精"的思想和相邻精度层能量变化差估计提出了一种改进迭代算法.仿真实验表明该方法能无限逼近能量极小点,大大提高了Paik型Hopfield网络的精度和收敛速度.

Image Restoration Based on the Modified Paik-Hopfield Neural Network

In this paper, a modified Paik-Hopfield neural network model based on the new state updating rule is proposed for restoring a degraded image. The convergence, the residual error and the energy change of the full parallel mode are thoroughly studied. An improved iteration algorithm based on the idea of "from coarse to fine" and the difference estimation between two adjacent layers is also presented. Experimental results demonstrate that this method can approximate the minimum of the energy infinitely and greatly improve the speed of convergence as well as the precision.