Detail-aware near infrared and visible fusion with multi-order hyper-Laplacian priors

Abstract

Heavy haze/noise can cause unpleasant information loss in near infrared (NIR) and visible (VI) image fusion. To generate high-quality fused images, this paper proposes a detail-aware near infrared and visible image fusion method, which establishes a detail injection variational framework based on multi-order hyper-Laplacian priors (MHLP). As the core idea, the MHLP is a l1/2 norm penalty term with first-order and second-order gradient differences, and applies sparsity-promoting and complete-comprehensive injection terms to enhance salient structures and fine-scale details. Besides, a noise map is introduced to suppress the structure inconsistency caused by heavy noise. Combining MHLP and a noise map, a novel near infrared and visible fusion framework is organized to deal with harsh environment imaging problems. The framework recovers visual visibility and preserves the significant scenery structural information and intrinsic colors. The video fusion of near infrared and visible also can be realized by the framework, which introduces the inter-frame coherence of successive frames additionally to retain the information lost in the degraded scene. Experiment results on public datasets and industrial sites demonstrate the advantages of our method from both qualitative and quantitative perspectives. The code is available at: https://github.com/BOYang-pro/MHLP.