Low-Light Image Enhancement Guided by Hybrid Explicit-Implicit Semantics

Low-light image enhancement (LLIE) is fundamentally a restoration problem rather than a brightness adjustment task, as it aims to recover the underlying scene information from images captured under low signal-to-noise ratio conditions. However, most existing methods adopt globally uniform enhancement schemes and overlook the degradation discrepancies across semantic regions, limiting overall performance. Recent semantic-guided LLIE methods often neglect how low-light degradation impairs the fidelity of semantic priors, preventing them from exploiting semantic information. To address these issues, we propose an implicit semantic guidance branch that employs a Light-up Module and a Feature Alignment Module to obtain accurate semantic features and adaptively integrate them into the feature representation space. Furthermore, we introduce an explicit semantic guidance branch that constructs a Semantic-Guided Pixel Consistency Loss using accurate semantic maps obtained by SAM 3, mitigating segmentation errors caused by cross-dataset label space discrepancies. Additionally, we develop a carefully designed low-light degradation model to synthesize training data and adopt a pre-training strategy, enabling the network to learn fine-grained semantic-aware brightness mappings. Based on these components, we develop a hybrid explicit–implicit semantic guidance framework. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in image quality, noise suppression, and structure preservation.