基于量子萤火虫和增益Beta的医学DR图像自适应增强

Medical DR Image Adaptive Enhancement Based on Quantum Glowworm and Gain Beta

为了解决医学DR图像对比度低、边缘模糊、细节不清晰和缺乏自适应增强的问题,提出一种基于量子萤火虫和增益Beta的医学DR图像自适应增强方法.该方法针对传统Beta变换的不足,提出了增益Beta变换方法,提出了应用于增益Beta变换的二分类别判定方法和参数约束修正方法.为实现自适应的增强,将量子计算和萤火虫群算法结合提出一种量子萤火虫群算法,提出一种FHCE图像增强质量评价标准作为算法的适应度函数,该算法可快速精确求解应用于医学DR图像自适应增强的增益Beta变换的最优增强参数值.实验结果表明这种方法提高了医学DR图像的对比度,边缘和细节更加的清晰,能够自适应增强各类型医学DR图像.

According to the problem about medical DR image that contrast is reduced ,edge is vague ,details are buried ,it is short of adaptive enhancement ,method of medical DR image adaptive enhancement based on quantum glowworm and gain Beta is proposed .A gain Beta transformation method is put forward aiming at the shortage of traditionalBeta transformation . A dichotomy category determination method and parameter constraint revision method applied to gain Beta transformation are given .In order to realize the adaptive enhancement ,a quantum glowworm swarm algorithm based on quantum computation and glowworm swarm algorithm is proposed .A HHCE image enhancement quality evaluation criteria is defined ,and it is used as fitness function of this algorithm .Optimal enhancement parameters of gain Beta transformation applied to medical DR image adaptive enhancement are computed fleetly and precisely .Simulation experiment on this method about medical DR image shows that contrast is raised ,edge and details are clearer ,adaptive enhancement on different kinds of medical DR images is realized .