A Mask-Guided Latent Low-Rank Representation Method for Infrared and Visible Image Fusion

Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing key information extraction. In the fusion stage, a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions, while an average fusion strategy improves background smoothness and stability. Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI, MI, Qabf, PSNR, and EN metrics, effectively preserving salient target details while maintaining balanced background information. Compared to state-of-the-art fusion methods, our approach achieves more stable and visually consistent fusion results. The fusion code is available on GitHub at: (accessed on 15 January 2025).