计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (25): 195-197.DOI: 10.3778/j.issn.1002-8331.2009.25.060

• 图形、图像、模式识别 • 上一篇    下一篇

基于自适应小生境遗传算法的图像复原

狄金海   

  1. 浙江工贸职业技术学院,浙江 温州 325003
  • 收稿日期:2008-09-12 修回日期:2008-10-13 出版日期:2009-09-01 发布日期:2009-09-01
  • 通讯作者: 狄金海

Adaptive niche genetic algorithm for image restoration

DI Jin-hai   

  1. Zhejiang Vocational and Technical College,Wenzhou,Zhejiang 325003,China
  • Received:2008-09-12 Revised:2008-10-13 Online:2009-09-01 Published:2009-09-01
  • Contact: DI Jin-hai

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0.2~0.3 dB,且运算时间仅有它们的40%~50%。

关键词: 图像复原, 遗传算法, 小生境算法, 邻居模型

Abstract: 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 multi-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’.

Key words: image restoration, genetic algorithm, niche algorithm, neighborhood model

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