Low-light image enhancement aims to recover clear images from low-illumination observations and is crucial for high-level downstream vision tasks. However, existing methods frequently encounter color distortion and structural artifacts when balancing global smooth illumination adjustment and local high-frequency detail recovery. To address these issues, we propose CGS-Retinex as the first low-light image enhancement framework based on explicit-implicit joint modeling. Our framework deeply integrates continuous Gaussian splatting with Retinex theory. Specifically, we represent the image grid as a continuous parameter field and propose a continuous Gaussian renderer to estimate the spatially continuous global illumination distribution. This approach fundamentally eliminates grid artifacts caused by discrete Gaussian sampling. Furthermore, we introduce an implicit neural representation to model reflectance independently. We leverage shallow high-frequency features to guide the network in accurately reconstructing degraded texture details. Within the Retinex framework, we incorporate physics-inspired brightness consistency constraints and illumination smoothness regularization to enable explicit illumination and implicit reflectance to maintain proper exposure and achieve high-fidelity recovery of high-frequency structures and colors. Extensive experiments demonstrate that CGS-Retinex significantly suppresses dark-region noise and overexposure while achieving exceptional high-frequency structural fidelity and color restoration by precisely decoupling illumination and texture. This work establishes a novel continuous physical representation paradigm for low-light image enhancement.

翻译：低光照图像增强旨在从低照度观测中恢复清晰图像，对高级下游视觉任务至关重要。然而，现有方法在平衡全局平滑光照调整与局部高频细节恢复时，常出现色彩失真与结构伪影。为解决这些问题，我们提出CGS-Retinex作为首个基于显式-隐式联合建模的低光照图像增强框架。该框架深度融合了连续高斯Splatting与Retinex理论。具体而言，我们将图像网格表示为连续参数场，并提出连续高斯渲染器以估计空间连续的全局光照分布。该方法从根本上消除了离散高斯采样导致的网格伪影。此外，我们引入隐式神经表示独立建模反射分量，利用浅层高频特征引导网络精确重建退化纹理细节。在Retinex框架中，我们融入物理启发的亮度一致性约束与光照平滑正则化，使显式光照与隐式反射保持恰当曝光，并实现高频结构与色彩的高保真恢复。大量实验表明，CGS-Retinex通过精确解耦光照与纹理，显著抑制暗区噪声与过曝，同时实现卓越的高频结构保真度与色彩复原。本工作为低光照图像增强建立了新颖的连续物理表示范式。