用扩散模型修复色卡，让照片颜色跨相机自适应。
GCC: Generative Color Constancy via Diffusing a Color Checker
- 用扩散模型直接补全色卡，推断光照信息。
- 在不同相机上测试，颜色还原误差降低18%。
- 适合需要跨设备一致色彩的摄影与视觉应用。
颜色恒常性方法常因相机传感器光谱响应差异而难以泛化。本文提出GCC，利用扩散模型将色卡补全至图像中以估计光照。核心创新包括：（1）单步确定性推理，直接生成反映场景光照的色卡；（2）拉普拉斯分解技术，在保持色卡结构的同时实现光照依赖的颜色自适应；（3）基于掩码的数据增强策略，应对色卡标注不精确的问题。借助预训练扩散模型丰富的先验知识，GCC在跨相机复杂场景中表现出强鲁棒性。实验表明，该方法无需针对特定传感器训练，即可有效泛化于不同相机特性，为真实世界应用提供了一种通用且实用的解决方案。
原文摘要 · Abstract (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. By harnessing rich priors from pre-trained diffusion models, GCC demonstrates strong robustness in challenging cross-camera scenarios. These results highlight our method's effective generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile and practical solution for real-world applications.
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