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

## TL;DR

This paper introduces a new image fusion method that improves the clarity and visibility of important features in infrared and visible images.

## Contribution

The novel method uses sub-window variance filter and weighted least squares optimization for better image fusion without deep learning.

## Key findings

- The method outperforms nine state-of-the-art techniques in image quality and salient feature highlighting.
- It preserves texture details and avoids image distortion without needing large training datasets.
- Future work will address handling complex scenes through adaptive optimization and deep learning integration.

## Abstract

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.