Breaking Low-Light Fusion Barrier: Unsupervised Darkness and Noise-Aware Visible and Infrared Image Fusion Network

Infrared and visible image fusion aims to integrate complementary information but suffers from severe residual degradations under low-light conditions. Existing methods face two main limitations: darkness-aware fusion focuses on illumination enhancement while neglecting noise suppression, and degradation-aware supervised fusion relies on paired data and struggles with enhancement-denoising imbalance due to multi-task optimization conflicts. To address these issues, we propose BLFusion, an unsupervised darkness- and noise-aware fusion framework that performs illumination enhancement and noise suppression in two dedicated stages while fusing complementary information without high-quality references. First, a Retinex-guided state space model-based decomposition network models illumination degradation to brighten dark visible images. Then, an unsupervised denoising fusion network jointly performs fusion and denoising, where noise correlation is disrupted by shuffling and a blind-spot network with dilated convolutions estimates clean representations from surrounding pixels. Finally, noise-free features from both modalities are fused to generate the final image. Moreover, we construct the MRLL dataset with 500 well-aligned infrared-visible image pairs, filling the gap for real-world noise-degraded nighttime scenarios. Experiments demonstrate that BLFusion outperforms state-of-the-art methods and generalizes robustly across diverse low-light and noisy conditions. The MRLL dataset and code are publicly available at https://github.com/ChenDoubleJ/BLFusion-MRLL.