CGLEN for unsupervised low-light image enhancement with PDE-guided structural continuity refinement
TL;DR
A continuity-guided low-light enhancement network, termed CGLEN, is proposed for unsupervised structure-aware image reconstruction, and indicates that explicitly modeling structural continuity provides an effective strategy for unsupervised low-light image enhancement, particularly in challenging illumination conditions.
Abstract
Low-light image enhancement is commonly formulated as an illumination recovery problem. However, severe illumination degradation not only reduces image brightness but also weakens structural responses, causing edge discontinuities and unstable texture reconstruction. To address these challenges, low-light enhancement is reconsidered as a structure-constrained reconstruction problem, where illumination recovery and structural continuity preservation are jointly optimized. Although supervised methods can achieve promising enhancement quality, their reliance on paired low-light and normal-light images limits their applicability to diverse real-world scenarios. Unsupervised approaches provide a more flexible solution by avoiding the requirement for paired training data. In this work, a continuity-guided low-light enhancement network, termed CGLEN, is proposed for unsupervised structure-aware image reconstruction. CGLEN introduces a learnable Retinex decomposition module to estimate illumination and reflectance components, followed by a gradient-guided structural representation that provides reliable structural cues during enhancement. Furthermore, a PDE-inspired structural continuity refinement strategy is developed by incorporating gradient variation and Laplacian consistency into a lightweight residual propagation framework, enabling spatial continuity preservation under degraded illumination conditions. A structure-guided modulation mechanism, together with an auxiliary reconstruction branch and adaptive fusion strategy, is further introduced to improve optimization stability and reconstruction consistency. Extensive experiments on multiple benchmark datasets demonstrate that CGLEN achieves competitive enhancement performance compared with existing supervised and unsupervised methods, while maintaining relatively low computational complexity. The results indicate that explicitly modeling structural continuity provides an effective strategy for unsupervised low-light image enhancement, particularly in challenging illumination conditions.