超越固定亮度：迈向全色与正色图像上色
Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization
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中文总结 AI 辅助
该研究针对固定亮度图像上色系统在正色摄影输入下不可靠的问题，提出基于基础图像编辑模型的亮度无关框架，结合混合灰度目标训练，提升了正色输入上色的鲁棒性并减少色彩伪影。
中文摘要 AI 辅助
大多数图像上色系统在Lab色彩空间中运行，通过预测色度（ab）来保留输入衍生的亮度通道（L）。尽管在标准基准上有效，但这种固定亮度设计限制了亮度变化，且当灰度形成偏离自然图像亮度（如历史正色摄影）时变得不可靠。我们提出一种与亮度无关的上色框架，将上色表述为使用基础图像编辑模型的全RGB图像编辑。为衔接现代全色与历史正色条件，我们引入混合灰度目标，在标准亮度灰度与对红色不敏感的灰度形成下训练模型。在COCO、ImageNet及多实例基准上的实验表明，我们的方法在标准灰度输入上具有竞争力，且在正色输入上显著更鲁棒，定性对比与人类研究显示可见色彩伪影更少。
英文摘要
Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$). While effective on standard benchmarks, this fixed-luminance design restricts brightness changes and becomes unreliable when grayscale formation deviates from natural-image luminance, as in historical orthochromatic photography. We propose a luminance-agnostic colorization framework that formulates colorization as full-RGB image editing using a foundation image-editing model. To bridge modern panchromatic and historical orthochromatic conditions, we introduce a mixed grayscale objective that trains the model under both standard luminance grayscale and a red-insensitive grayscale formation. Experiments on COCO, ImageNet, and a multi-instance benchmark show that our method is competitive on standard grayscale inputs and substantially more robust under orthochromatic inputs, with qualitative comparisons and a human study indicating fewer visible color artifacts.
