Fusion of Low-Quality Visible and Infrared Images Based on Multi-Level Latent Low-Rank Representation Joint With Retinex Enhancement and Multi-Visual Weight Information

Latent low-rank representation has been applied to multi-level image decomposition for the fusion of infrared and visible images to obtain good results. However, when the original infrared and visible images are of low quality, the visual effect of the fused images is still unsatisfactory. To combat this challenge, this paper proposes an infrared and visible image fusion method based on multi-level latent low-rank representation joint with image enhancement and multiple visual weight information. First, the source images are decomposed into detail parts - including detail images and detail matrices - and the base images respectively using multi-level latent low-rank representation. Then the nuclear norm based fusion strategy is used to fuse the detail matrices and multi-visual weights determined by the clarity, local contrast and edge-corner saliency is used to fuse the detail images. The aforementioned two fusion results are weight averaged to obtain a fused detail image. The base images are fused by an averaging strategy after Retinex-based enhancement. The final fused image is obtained by combining the fused detail image and the fused base image. Compared with other state-of-the-art fusion methods, the proposed algorithm displays better fusion performance in both subjective and objective evaluation.