L3FMamba: Low-Light Light Field Image Enhancement With Prior-Injected State Space Models

Deyang Liu, Shizheng Li, Zeyu Xiao, Ping An, Caifeng Shan

Abstract: In this letter, we address the problem of low-light light field (LF) image enhancement, where spatial details and angular coherence are severely degraded due to noise and insufficient illumination. Existing methods often rely on local aggregation or naive view stacking, which fail to capture global illumination and long-range spatial-angular correlations. To overcome these limitations, we propose L3FMamba, a lightweight enhancement method that integrates Retinex and Atmospheric Scattering models with dark, bright, and average channel priors for robust illumination decomposition. Moreover, we incorporate a state space model to capture non-local spatial-angular dependencies, enabling effective propagation of global context across views. By combining physics-inspired priors with structured modeling, L3FMamba achieves accurate illumination correction and fine-detail preservation with minimal parameters. Experiments show that L3FMamba outperforms the state-of-the-art in quality.