基于压缩速度范围PSO的图像自适应增强

Adaptive Image Enhancement Based on Particle Swarm Optimization with Contracted Range of Velocity

基于对惯性权重ω和最大飞行速度Vmax的分析,结合完全覆盖图像增强典型变换函数类型的非完全Beta算子,提出压缩速度范围改进粒子群算法(CV-PSO)的灰度图像自适应增强方法。用于基本图像和交通图像的增强,并与基本及其他改进PSO算法做性能比较。实验结果证实了CV-PSO算法的有效性和优越性,且在视觉效果上优于传统直方图均衡化法。

Based on the analysis of inertia weight CO and the maximal flying speed Vmax , the improved Particle Swarm Optimization with Contracted range of search Velocity（CV-PSO） is proposed for the adaptive image enhancement. It combines with incomplete Beta operator which containes all different kinds of typical transformation functions. The algorithm is used for the basic and traffic images enhancement. It compares its performance with that of basic Particle Swarm Optimization（PSO） and other improved PSO. Results show that CV-PSO is effective and superior. Moreover, it is better than traditional histogram equalization method in visual quality.