基于自适应小生境遗传算法的图像复原  

Adaptive niche genetic algorithm for image restoration

作　　者：狄金海[1] 

机构地区：[1]浙江工贸职业技术学院,浙江温州325003

出　　处：《计算机工程与应用》2009年第25期195-197,共3页Computer Engineering and Applications

摘　　要：传统的小生境遗传算法收敛太慢,且容易陷入局部最优,对小生境算法做出以下三点改进:一是将解空间划分为多层区域,每层使用不同的距离因子;二是采用差值编码方式,使得算法更易收敛;三是使用伪并行加速算法,改进经典的邻居模型为镜像邻居模型。实验表明改进算法的PSNR比常用的遗传算法以及小生境算法高0.2～0.3dB,且运算时间仅有它们的40%～50%。Traditional Niche Genetic Algorithm（NGA） converges too slowly and is easy to trap in local extrema,thus it is modified in three aspects：Firstly,solution space is partitioned into muhi-layers,and distance factors are forced to vary with layers.Secondly, difference value coding method is adopted to guarantee that this proposed algorithm converges more easily.Finally,the traditional neighborhood model is meliorated into a repeat-neighborhood model.Simulations demonstrate that this Adaptive Niche Genetic Algorithm（ANGA） performs better than the popular GA and NGA.PSNR of ANGA is 0.2-0.3 dB higher than other algorithms while the consumed time of ANGA is only 40%-50% of other algorithms.

关 键 词：图像复原 遗传算法 小生境算法 邻居模型 

分 类 号：TN911.73[电子电信—通信与信息系统]