基于突变粒子群算法的图像自适应增强

利用突变粒子群算法自动获取图像非线性增强函数的最佳变换参数，达到图像增强的效果。该算法基于粒子群算法原理，采用针对图像质量评价效果的新适应度函数（包括方差、信息熵、紧致度、信噪改变量以及像素差别五要素），提出一种基于突变机制的粒子群算法，有效增大粒子间的差异性和非均匀性，打破平衡态，从而增强系统内动力以提高系统进化的效率。实验表明，该算法具有较高的自适应性，即避免了陷入局部极小，加快了收敛速度，且增强质量评价明显提高。

Image Adaptive Enhancement based on Mutational Particle Swarm Optimization

Automatically obtain the best transformation parameters of image nonlinear enhancement function based on the mutational particle swarm algorithm is proposed.This algorithm is based on particle swarm optimization principle and using a new fitness function suit for image quality evaluation(including the variance,information entropy,the firmness,the changing of signal and noise and the pixel difference),increasing the difference and non-uniformity between the particles effectively,and breaking the equilibrium,thereby enhancing the power system even to improve the efficiency of the system evolution.The experiments show that the algorithm has a higher selfadaptive,convergence speed up,and enhance the quality assessment significantly improved.