Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization

[Submitted on 11 Aug 2026 (v1), last revised 12 Aug 2026 (this version, v2)]

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Abstract: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.

Submission history

From: Swarnim Maheshwari [view email]
[v1] Tue, 11 Aug 2026 11:12:24 UTC (45,929 KB)
[v2] Wed, 12 Aug 2026 17:35:05 UTC (45,929 KB)