Saliency-enhanced infrared and visible image fusion via sub-window variance filter and weighted least squares optimization.

This paper proposes a novel method for infrared and visible image fusion (IVIF) to address the limitations of existing techniques in enhancing salient features and improving visual clarity. The method employs a sub-window variance filter (SVF) based decomposition technique to separate salient features and texture details into distinct band layers. A saliency map measurement scheme based on weighted least squares optimization (WLSO) is then designed to compute weight maps, enhancing the visibility of important features. Finally, pixel-level summation is used for feature map reconstruction, producing high-quality fused images. Experiments on three public datasets demonstrate that our method outperforms nine state-of-the-art fusion techniques in both qualitative and quantitative evaluations, particularly in salient target highlighting and texture detail preservation. Unlike deep learning-based approaches, our method does not require large-scale training datasets, reducing dependence on ground truth and avoiding fused image distortion. Limitations include potential challenges in handling highly complex scenes, which will be addressed in future work by exploring adaptive parameter optimization and integration with deep learning frameworks.