目的 可见光图像具有丰富的纹理信息,红外图像具有较强的目标指示信息,进行融合时只有合理地设计融合规则才能充分利用两者的互补信息,为此,提出一种基于效果评估的可见光与红外图像区域级反馈融合算法.方法 首先对待融合图像进行非下采样轮廓波变换(NSCT),将其分解为低频和高频部分.同时采用分形特征对红外图像进行人造目标增强,通过阈值分割得到目标区域与背景区域.在设计低频融合规则时,选取目标区域与背景区域的加权融合系数作为参数,根据图像融合效果评估的量化指标,运用遗传算法进行参数的优化求解.对高频部分采用基于区域的加权平均融合规则.最后,利用优化后的融合系数进行NSCT逆变换得到融合图像.结果 采用3组图像,结合主观评价和客观评价指标对4种融合算法的结果进行了比较分析,实验结果表明,本文算法融合后图像更自然,目标更显著,客观评价结果总体上最优.结论 本文算法有效结合了红外图像的目标信息与可见光图像的背景信息,融合图像具有更强的对比度,有利于进行战场态势显示和目标识别任务.

Objective A fusion rule must be reasonably designed to make full use of complementary information, namely, the rich texture information in visible images and the strong object-targeting information in infrared images.Method A visible and infrared image region-level feedback fusion algorithm is proposed on the basis of performance evaluation.The images to be fused are initially decomposed into their low-frequency and high-frequency parts via the nonsubsampled contourlet transform(NSCT). Meanwhile, fractal features are adopted to perform man-made object enhancement of infrared images.Object and background areas are then obtained through threshold segmentation. In the design of low-frequency fusion rule, the weighted fusion coefficients of the object and background areas are selected as parameters.A genetic algorithm is used to optimize these parameters in accordance with the performance evaluation of fusion results. A regional weighted average fusion rule is used to fuse high-frequency parts. Finally, inverse NSCT is performed to obtain the fusion image with the optimized parameters.Result Three sets of images are used to compare the performance of four fusion algorithms by subjective and objective evaluation. Experimental results demonstrate that the fusion images from the proposed algorithm are natural and that the objects in the images are notable.Furthermore, the objective evaluation result is optimal.Conclusion The proposed algorithm can combine the object information from infrared images and the background information from visible images.The resulting fusion images have a strong contrast, which is beneficial for battlefield situation display and object recognition tasks.