Adaptive Multi-Branch Feature Fusion for Low-Light Image Enhancement

Low-light image enhancement (LLIE) remains a challenging problem due to spatially varying illumination degradation, compressed tonal distributions, and structural detail loss. This paper presents Adaptive Multi-Branch Feature Fusion (AMBFF), a unified framework that formulates LLIE as a multi-domain representation alignment task. The proposed architecture explicitly models complementary feature domains, including hierarchical spatial context, luminance–chrominance decoupling, edge–texture structures, frequency-domain information, and differentiable tonal histogram representations. A spatially adaptive gating mechanism dynamically weights multi-feature branches through a convex fusion strategy, enabling location-aware illumination correction while preserving structural integrity and color fidelity. Extensive evaluations on widely used benchmark datasets demonstrate that AMBFF consistently outperforms representative conventional and deep learning-based approaches in terms of PSNR, SSIM, and LPIPS. Ablation analyses confirm the complementarity of the proposed feature domains and the robustness benefits of adaptive fusion. Despite its multi-branch design, AMBFF maintains a favorable performance–complexity trade-off, highlighting the effectiveness of structured multi-domain modeling for low-light image enhancement.