Superpixel-based adaptive salient region analysis for infrared and visible image fusion

Chengzhou Li, Kangjian He, Dan Xu, Dapeng Tao, Xu Lin, Hongzhen Shi, Wenxia Yin

Abstract: Infrared and visible image fusion aims to highlight the infrared target and preserve valuable texture details as much as possible. However, the infrared target needs to be more apparent in most image fusion methods. A large amount of infrared noise remains in the fusion results, significantly reducing the proportion of valuable texture details in the fusion results. How to highlight the salient of infrared targets, lower noise, and retain more valuable texture details in the fusion results still need to be solved. We propose an adaptive salient region analysis method based on superpixels (SSRA) for infrared and visible fusion to solve this problem. This method uses salient region analysis based on superpixels to highlight the salience region effectively. We design a texture detail fusion method based on brightness analysis of the visible image to suppress noise and keep more meaningful texture detail information. The experimental results show that our proposed method performs better in subjective vision and quantitative evaluation than some advanced methods. In addition, we also demonstrate that SSRA is capable of supporting high-level visual tasks well. Our code is publicly available at: https://github.com/VCMHE/SSRA.