突破固定亮度限制，让老照片和现代图像都能精准上色。
Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization
- 用基础图像编辑模型直接生成全彩图像，不依赖固定亮度通道。
- 在标准和历史黑白图像上均表现更优，尤其减少老照片上色的伪影。
- 适合修复历史影像、跨时代图像处理，对摄影史研究有实用价值。
当前图像着色系统多在 $Lab$ 空间中预测 $ab$ 色度分量，同时保留输入生成的亮度通道 ($L$)。尽管在标准基准上表现良好，但固定亮度设计限制了亮度调整，且在非自然亮度形成的灰度图像（如历史正色摄影）上可靠性下降。本文提出一种亮度无关的着色框架，将着色任务建模为使用基础图像编辑模型的全RGB图像编辑。为弥合现代全色与历史正色图像的差异，引入混合灰度目标，在标准亮度灰度与红光不敏感灰度两种条件下联合训练模型。在 COCO、ImageNet 及多实例基准上的实验表明，该方法在标准灰度输入下表现竞争力，且在正色输入下显著更鲁棒，定性对比与人类评估显示颜色伪影更少。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。