Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces

Extreme low-light image enhancement presents persistent challenges due to compounded degradations involving underexposure, sensor noise, and structural detail loss. Traditional low-light image enhancement methods predominantly employ global adjustment strategies that disregard semantic context, often resulting in incomplete detail recovery or color distortion. To address these limitations, we propose a semantic-aware knowledge-guided framework (SKF) that systematically integrates semantic priors for improved illumination recovery. Our framework introduces three key modules: A Semantic Feature Enhancement Module for integrating hierarchical semantic features, a Semantic-Guided Color Histogram Loss to enforce color consistency, and a Semantic-Guided Adversarial Loss to enhance perceptual realism. Furthermore, we improve the semantic-guided color histogram loss by leveraging multi-color space constraints. Inspired by human visual perception mechanisms, our enhanced loss function calculates color discrepancies across three color spaces—RGB, LAB, and LCH—through three components: lossrgb, losslab and losslch. These components collaboratively optimize image contrast and saturation, thereby simultaneously enhancing contrast preservation and chromatic naturalness.