基于混沌粒子群优化的Contourlet域红外图像自适应增强

Infrared image adaptive enhancement in Contourlet domain based on chaotic particle swarm optimization

为进一步增强红外图像的对比度、提高清晰度并抑制噪声,提出了一种基于混沌粒子群优化(PSO)的Contourlet域红外图像自适应增强方法。首先对红外图像进行Contourlet变换,调整低通图像和细节图像在原始图像中的比例,并经灰度线性拉伸增强图像对比度;然后通过非线性增益函数调整含噪带通方向子带系数;利用兼顾对比度、清晰度和信噪比3个指标的定量综合评价函数作为混沌PSO的适应度,寻找基于Contourlet的空间域增强和带通方向子带系数调整的非线性增益函数所涉及的最优参数。大量实验结果表明:与近年提出的4种图像增强方法相比,该方法能使红外图像的对比度和清晰度提高,噪声降低,整体视觉效果更佳。

To further enhance the contrast of infrared image, improve the definition and suppress the noise, an adaptive enhancement method in Contourlet domain based on chaotic Partic[e Swarm （）ptimization （PSO） is proposed. First, Contourlet transform of the infrared image is performed. The proportion of low-pass image and detail image in the original image is adjusted, and the contrast is enhanced by linear gray stretch. Then, the coefficients of noisy bandpass directional subbands are adiusted by nonlinear gain function. An integrated quantitative evaluation function is used as the fitness of the chaotic PSO. In this evaluation function three indexes are taken into account, i. e.