一种图像增强的自适应免疫遗传算法

A Method of Adaptive Genetic Immune Algorithm for Image Enhancemengt

图像增强处理中,Tubbs曾将几种常用的非线性变换函数表示成一个归一化的非完全Beta函数,进行图像增强方面的研究,但确定Beta函数的参数仍是一个复杂的问题。现将自适应免疫遗传算法应用到图像的增强处理中,利用自适应免疫遗传算法的快速搜索能力,对给定的测试图像,自适应地变异、搜索、直至最终确定变换函数的最佳参数α,β值,从而实现图像的自适应增强。与穷举法相比,大大节约了求解的时间和计算的复杂度,提供了一个解决图像增强方面问题的途径。现通过对自然图像的仿真实验可以看出上述方法的有效性。

In image enhancement, Tubbs proposed a normalized incomplete Beta function to represent the four kinds of non-linear transform functions most commonly used. But how to adaptively define the coefficients of the Beta function is still a problem. Applying the adaptive irnmune algorithm in one image enhancement, we utilize the quickly search ability of the algorithm, adaptive variation, search, at last searches the optimal of suboptimal coefficients. It is largely saved time of acquired answer and compute complexity, therefore it is an efficient approach of image enhancement. Compared with the common image adjustment approach, our method is more efficient and powerful.