多粒子群优化算法的远红外图像对比度增强

Far Infrared Image Contrast Enhancement Based on Multi-Particle Swarm Optimization

研究红外图图像增强优化处理问题,针对标准粒子群优化算法(Particle Swarm Optimization,PSO)在远红外图像对比度增强处理过程中计算速度慢、算法进化到后期收敛速度慢和早熟问题,通过分析标准粒子群算法粒子的运动行为,提出了一种改进的粒子群优化算法和非完全Beta函数相结合的自适应图像对比度增强的算法。在传统的粒子群优化算法的基础上,改进的算法融入了"多粒子群"和"进化论"等理论方法,利用多个粒子彼此独立的搜索空间最优解,提高了全局搜索能力;且在迭代过程中,适时调整加速因子,便于算法在迭代后期找到全局最优解。通过对远红外图像进行仿真,结果表明,改进算法在计算速度和收敛性方面均优于标准粒子群算法,不依赖于图像具体内容,具有较好的通用性和推广价值。

In order to deal with the problems of slow calculation process, slow convergence speed and premature of particle swarm optimization （PSO） algorithm in the processing of far infrared image contrast enhancement, this pa- per studied the movement of particles of PSO algorithm, and presented adaptive infrared image contrast enhancement based on modified particle swarm optimization and incomplete Beta Function. On the basis of traditional PSO, an im- proved PSO was integrated into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability was improved. And in the iteration proce- dures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO algorithm is better than the standard PSO in computing speed and convergence, and it does not depend on the specific content of the im- age to be enhanced. The proposed algorithm has good versatility and value to promote.