基于双粒子群优化算法的图像盲复原

采用模糊图像与复原图像的均方误差作为优化的性能指标是传统的图像盲复原通常算法，复原结果常与人类主观视觉效果不一致。为进一步提高复原效果，本文结合反映人类视觉特性的Weber定律，提出一种改进的图像盲复原优化性能指标，并且采用双粒子群交替最小化算法进行求解，即在模糊辨识阶段，采用一个粒子群优化算法求解点传播函
数；在复原阶段，采用另一个粒子群优化算法求解复原图像。仿真实验表明，该算法比以前的算法有更好的复原效果。

A Method for Blind Image Restoration Based on Double PSOs

The mean square error (MSE) between a fuzzy image and its restored image is usually used as the criterion in traditional blind image restoration methods, which results in the bad effect in human vision. In order to get finer restoration images,a modified blind image restoration criterion is presented in this paper, which combines with the model of visual features (Weber's law). In addition, based on a double particle swarm optimization (PSO) algorithm,an iterative scheme using alternating minimization is devised to recover the image and simultaneously identify the Point Spread Function (PSF). The first PSO focuses on evolving PSF in the process of identification.At the same time,the second PSO focuses on evolving the recovered image in the process of restoration.The experimental results show a superior performance compared to the previous approach.