Infrared and visible image fusion based on edge-preserving filter and weighted least square optimization

Abstract

Infrared (IR) and visible (VI) image fusion play an important role in improving ability to scene perception and target detection, however, due to different imaging principles, significant feature differences of images make it very difficult to extract and integrate feature information effectively, especially in complex scenes where the target feature has different scales and contrast. Therefore, this paper proposes an image fusion method based on scale-aware edge-preserving filter and weighted least square optimization, aiming to extract features at different scales more accurately. First, we designed a hybrid feature decomposition method based on the scale-aware structure-preserving filter and Gaussian filter. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer-scale division than traditional multiscale decomposition methods. Then, according to the characteristics of infrared and visible images in the region layer and texture layer, the weighted least squares optimization framework is used combing with visual saliency map and scale-aware mechanism respectively, to obtain better visual expression effect. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state-of-the-art methods.