GCC: Generative Color Constancy via Diffusing a Color Checker
Authors: name: Chen-Wei Chang, name: Cheng-De Fan, name: Chia-Che Chang, name: Yi-Chen Lo, name: Yu-Chee Tseng, name: Jiun-Long Huang, name: Yu-Lun Liu

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.