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. 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. 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. 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.