微粒群优化和视觉感应相结合的图像增强方法

Combining intelligent optimization and visual influence for image enhancement

对粒群优化算法进行了改进,提出了一种微粒群优化和视觉感应相结合的图像增强方法,通过微粒群算法优化灰度图像的平均明暗信息熵差值,自适应地选择图像灰度转换函数,用以实现图像的增强。该方法不仅参数个数少,优化速度快,在搜索能力上优于粒群优化算法,而且能够保证算法的全局收敛性。仿真实例证明了该方法在图像增强上的有效性和优越性。

Particle Swarm Optimization（PSO） algorithm is improved,and the method about combining intelligent optimization and visual influence for image enhancement is proposed.By optimizing the difference of averag bright and dark information entropy of a gray image,the gray transformation function of an image is adaptively chosen.Lower parameters of this method are needed,higher optimizing speed of the method is possessed,searching ability of the method is superiority,and global convergence of the mothed is guaranteeed.The efficiency and superiority of this mothed can be confirmed by the simulation results.