Deep learning techniques have revolutionized the infrared and visible image fusion (IVIF), showing remarkable efficacy on complex scenarios. However, current methods do not fully combine frequency domain features with global semantic information, which will result in suboptimal extraction of global features across modalities and insufficient preservation of local texture details. To address these issues, we propose Wavelet-Mamba (W-Mamba), which integrates wavelet transform with the state-space model (SSM). Specifically, we introduce Wavelet-SSM module, which incorporates wavelet-based frequency domain feature extraction and global information extraction through SSM, thereby effectively capturing both global and local features. Additionally, we propose a cross-modal feature attention modulation, which facilitates efficient interaction and fusion between different modalities. The experimental results indicate that our method achieves both visually compelling results and superior performance compared to current state-of-the-art methods. Our code is available at https://github.com/Lmmh058/W-Mamba.

Abstract Infrared image enhancement is an effective way to solve contrast reduction or details degradation in infrared imagery. An infrared enhancement approach based on local saliency extraction is proposed here. First, saliency maps are extracted within a local window by combining spatial weight. Second, with the change of the window size, potential targets and details in different sizes can be extracted. Considering window sizes as the scales, the saliency maps are obtained and infrared images are enhanced at different scales, and finally, multi‐scale fusion is used to achieve the enhancement. Eight popular infrared enhancement approaches are introduced for comparison. Subjective qualitative observation experiments show that our strategy based on local saliency analysis and multi‐scale fusion can well extract potential targets and areas of varying size from the source images with different sizes, to obtain a good enhancement effect. Meanwhile, we introduce six objective evaluation methods to measure the results, and the evaluation data to prove the effectiveness of the proposed algorithm. The experiments also indicate the real‐time processing capability of the proposed method. The proposed method is finally well applied in the hardware system and shows its good performance.