Continuous Splatting meets Retinex: Continuous Gaussian Splatting and Implicit Reflectance Modeling for Low-Light Image Enhancement

2026-06-14

AI Summary

This work addresses the challenge of low-light image enhancement, where balancing global illumination adjustment and local high-frequency detail recovery often leads to color distortion and structural artifacts. The paper introduces continuous Gaussian splatting into this task for the first time, proposing a novel continuous physical representation paradigm grounded in Retinex theory. By jointly modeling illumination and reflectance through an explicit–implicit framework, the method employs a continuous Gaussian renderer to estimate spatially smooth global illumination while representing reflectance via an implicit neural function, guided by high-frequency features for texture reconstruction. Brightness consistency constraints and illumination smoothness regularization are incorporated to eliminate grid-like artifacts caused by discrete sampling, enabling precise decoupling of illumination and texture. This approach effectively suppresses noise and overexposure while significantly improving high-frequency structural fidelity and color accuracy, outperforming existing methods.

Abstract

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.

Problem

• Low-light image enhancement
• Color distortion
• Structural artifacts
• Illumination adjustment
• Detail recovery

Innovation

• Continuous Gaussian Splatting
• Implicit Neural Representation
• Retinex Theory
• Low-Light Image Enhancement
• Explicit-Implicit Joint Modeling