Paper · arXiv 2502.17435
GCC: Generative Color Constancy via Diffusing a Color Checker
Chen-Wei Chang, Cheng-De Fan, Chia-Che Chang, Yi-Chen Lo, Yu-Chee Tseng, Jiun-Long Huang, Yu-Lun Liu
GCC utilizes diffusion models to robustly estimate illumination by inpainting color checkers, achieving state-of-the-art results in cross-camera evaluations without sensor-specific training.
Abstract
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.