改进的自适应免疫遗传算法在图像增强中的应用

针对传统图像增强方法中图像细节丢失、图像对比度不明显以及方法普适性差等缺点，提出了一种自适应免疫遗传算法用于图像增强。该算法与传统遗传算法的不同在于引入免疫算子抑制优化过程中出现的退化现象，根据个体适应度自适应调整遗传算子的概率值和基因变异位数，从而增强了种群多样性，提高了算法快速性和全局收敛性。实验结果表明：基于该算法的图像增强具有图像细节清楚、对比度强、方法普适性强等优点。

Application of improved adaptive immune genetic algorithm in image enhancement

Aiming at disadvantages in traditional image enhancement methods,such as disappearance of image detail,unsharp image contrast and poor method universality,propose an image enhancement method based on adaptive immune genetic algorithm( AIGA). The differences between AIGA and standard genetic algorithm( SGA) are that the former restrains degradation phenomena appeared in the process of optimization by introducing the immune operator,and the probability of genetic operator and the digits of gene mutation are varied adaptively depending on the fitness of individual,thus improving the performance in population diversity,searching speed and global convergence. Experimental results show that the proposed algorithm can make the image detail clearly,make the contrast enhancement,and has strong universality.