Probabilistic-Based Learning for Joint Light Field Image Compression and Enhancement Under Low-Light Conditions

Light field (LF) imaging has attracted increasing research interest in challenging illumination conditions due to its ability to provide rich spatial and angular cues. However, such data present dual challenges: 1) the inherent multi-view structure introduces substantial data redundancy, creating high demands for efficient compression; 2) the insufficient illumination leads to severe quality degradation, which weakens inter-view consistency and visual perception. To address these coupled factors, we propose a Probabilistic-based learning for joint LF image compression and enhancement under low-light conditions (PrL-LFCE). The framework unifies structure-aware compression and feature enhancement mechanisms by introducing learnable probabilistic modeling into both feature coupling and latent distribution estimation to adaptively handle the uncertainty induced by illumination degradation and compression-related information loss. Specifically, we design a probability-based multi-directional feature coupling module that dynamically balances structural preservation and redundancy reduction across multiple directionally arranged sub-aperture images. Moreover, we introduce a swin-gated enhancement module that suppresses noise and highlights structurally salient regions in compression-aware feature representations through attention-guided gating. Extensive experiments show that PrL-LFCE consistently outperforms state-of-the-art methods, achieving at least 34.86% bitrate savings while maintaining excellent visual quality, demonstrating a strong joint compression and enhancement capability.