发表机构
Fuzhou University（福州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CAGE框架，通过AdaLAB色彩空间与AdaCCT变换实现色彩去偏与饱和度校正，在多基准实验中提升低光照图像增强的色彩保真度与视觉质量。
AI 中文摘要
低光照成像常因低信噪比和图像形成过程引入色彩偏差。尽管近期低光照图像增强方法已实现较强的亮度恢复，但色彩保真恢复仍具挑战性，表现为整体色彩偏差及局部欠饱和与过饱和。为解决该问题，本文提出CAGE，即一种兼具自适应色彩去偏与色域协调饱和度校正的圆柱色彩校正框架，用于实现色彩保真的低光照图像增强。首先，引入AdaLAB，一种圆柱自适应LAB色彩空间，为均匀色彩校正提供解耦且针对图像的基准。基于该色彩空间，进一步开发AdaCCT，一种自适应圆柱色彩变换，包含正向与反向变换，用于RGB与AdaLAB色彩空间的转换，以及必要的色彩去偏与饱和度校正。正向变换通过色度平面的移位与缩放重组色度分布，在主干网络增强前抑制嵌入的色彩偏差；反向变换通过超色域亮度补偿实现保真的饱和度校正。在多个基准上的大量实验表明，CAGE实现了更保真的色彩恢复，具体减少了色彩偏差与饱和度异常，且在不同低光照增强主干网络上均提供更优的整体视觉质量。代码可在指定URL获取。
英文摘要
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/.
CommentsAccepted by ACMMMM 2026
