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This paper introduces CAGE, a cylindrical color correction framework designed to enhance low-light images by addressing color bias and saturation issues. By employing AdaLAB, an adaptive LAB color space, and AdaCCT, an adaptive cylindrical color transform, the method effectively suppresses embedded color bias and rectifies saturation abnormalities. Experimental results demonstrate that CAGE significantly improves color fidelity and visual quality compared to existing low-light enhancement techniques across various benchmarks.
CAGE achieves unprecedented color fidelity in low-light image enhancement by effectively debiasing color and rectifying saturation, outperforming traditional methods.
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/.
