Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification

2026-08-11

AI Summary

Low-light image enhancement often suffers from color distortion due to global color casts and local saturation anomalies. To address this, this work proposes the CAGE framework, which first constructs an image-adaptive cylindrical LAB color space (AdaLAB) and employs an adaptive cylindrical color transformation (AdaCCT) prior to enhancement to suppress color casts. Following enhancement, saturation correction is achieved through chroma plane translation and scaling combined with out-of-gamut luminance compensation, ensuring gamut-coherent color reproduction. By decoupling color cast correction from saturation restoration, the method significantly reduces color distortion and improves visual quality across multiple benchmarks, while remaining flexibly integrable into diverse low-light enhancement backbone networks.

Abstract

Low-light imaging often introduces color bias caused by the low signal-to-noise ratio and the image formation process. Although recent low-light image enhancement methods have achieved strong brightness recovery, faithful color restoration remains challenging, manifesting as overall color bias together with local under- and over-saturation. To address this issue, we propose CAGE, a cylindrical color correction framework with adaptive color debiasing and gamut-harmonized saturation rectification for color-faithful low-light image enhancement. We first introduce AdaLAB, a cylindrical adaptive LAB color space that provides a decoupled and image-specific basis for uniform color correction. Building on this color space, we further develop AdaCCT, an adaptive cylindrical color transform with forward and inverse transforms for the conversion between RGB and AdaLAB color space, as well as necessary color debiasing and saturation rectification. The forward transform suppresses embedded color bias before backbone enhancement by reorganizing the chromatic distribution through chromatic-plane shifting and scaling, while the inverse transform achieves faithful saturation rectification through out-of-gamut lightness compensation. Extensive experiments on multiple benchmarks show that CAGE achieves more faithful color restoration, specifically reduces color bias and saturation abnormality, and delivers better overall visual quality across different low-light enhancement backbones. The code is available at https://yangzhichen763.github.io/CAGE/.

Problem

• low-light image enhancement
• color bias
• saturation rectification
• color-faithful restoration

Innovation

• adaptive color debiasing
• saturation rectification
• cylindrical color space
• color-faithful enhancement
• AdaCCT