用中性中间表示实现稳定参考色调映射，避免颜色失真。
CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation
- 引入中性中间表示，分两阶段完成色调迁移
- 在多个数据集上优于当前最优方法，结果更自然一致
- 适合需要高质量调色的影视与摄影场景
参考图像色调调整旨在还原参考图像的明暗氛围和光照效果，同时保持色彩和谐与场景结构。现有基于照片真实感或滤镜的方法常出现色调映射不稳定——过度偏移或颜色保留不一致——导致结果不自然。本文提出 CanonCGT，一种基于中性基准（canonical pivot）的两阶段框架，实现稳定色调映射。第一阶段将输入图像归一化以消除内在色调偏差；第二阶段将其映射至参考风格。采用双阶段训练方案 DP-CGT，结合有监督预设学习与无配对图像的自监督优化。CanonCGT 在多个数据集上均实现高保真、色调一致的视觉效果，显著超越现有最先进方法。代码已公开于 https://github.com/Jinwon-Ko/CanonCGT。
原文摘要 · Abstract (English)
Reference-based color grading aims to reproduce the tonal mood and lighting of a reference while preserving color harmony and scene structure. Existing photorealistic and filter-based methods often produce unstable tone mappings -- over-shifting or inconsistently retaining colors -- leading to unnatural results. We propose CanonCGT, a two-stage framework built on a canonical pivot -- a style-neutral intermediate representation for stable color mapping. The first stage canonicalizes the input by removing intrinsic tonal bias, and the second color-grades it to match the reference style. A dual-phase training scheme, DP-CGT, combines supervised preset learning with self-supervised refinement on unpaired photographs. CanonCGT delivers photorealistic and tonally consistent results across diverse datasets, surpassing state-of-the-art methods in stability and visual fidelity. Our codes are available at \href{https://github.com/Jinwon-Ko/CanonCGT}{https://github.com/Jinwon-Ko/CanonCGT}
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