GCC：基于色彩校验卡扩散的生成式色彩恒常性

GCC: Generative Color Constancy via Diffusing a Color Checker

February 24, 2025

作者: Chen-Wei Chang, Cheng-De Fan, Chia-Che Chang, Yi-Chen Lo, Yu-Chee Tseng, Jiun-Long Huang, Yu-Lun Liu

cs.AI

摘要

色彩恒常性方法往往难以在不同相机传感器之间实现泛化，这主要是由于光谱敏感度的差异所致。我们提出了GCC方法，该方法利用扩散模型将色卡修复到图像中以进行光照估计。我们的核心创新包括：(1) 一种单步确定性推理方法，能够修复反映场景光照的色卡；(2) 一种拉普拉斯分解技术，在保持色卡结构的同时允许光照依赖的颜色适应；(3) 一种基于掩码的数据增强策略，用于处理不精确的色卡标注。GCC在跨相机场景中展现出卓越的鲁棒性，在双向评估中实现了5.15°和4.32°的最差25%误差率，达到了当前最佳水平。这些结果凸显了我们的方法在不同相机特性下的稳定性和泛化能力，且无需传感器特定的训练，使其成为现实应用中的多功能解决方案。

English

Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. GCC demonstrates superior robustness in cross-camera scenarios, achieving state-of-the-art worst-25% error rates of 5.15{\deg} and 4.32{\deg} in bi-directional evaluations. These results highlight our method's stability and generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile solution for real-world applications.