GCC: Generative Color Constancy via Diffusing a Color Checker
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.
67bd6b4b8edd1ce8ad5603a0
Chen-Wei Chang
67bd6b4b8edd1ce8ad5603a1
Cheng-De Fan
admin_assigned
2025-02-25T16:08:08.047Z
64ea1e12925565abda02b17b
/avatars/b2bc33d95a147c6c8cf6b54672eb5a97.svg
Cheng-De  Fan
user
fansam39
67bd6b4b8edd1ce8ad5603a2
Chia-Che Chang
67bd6b4b8edd1ce8ad5603a3
Yi-Chen Lo
67bd6b4b8edd1ce8ad5603a4
Yu-Chee Tseng
67bd6b4b8edd1ce8ad5603a5
Jiun-Long Huang
67bd6b4b8edd1ce8ad5603a6
Yu-Lun Liu
admin_assigned
2025-02-25T16:08:30.752Z
6459d5da3b6fafd9664807ab
/avatars/57430d1bbde3a2fe5586e5fbcafb0e74.svg
Yu-Lun Liu
user
yulunliu
GCC: Generative Color Constancy via Diffusing a Color Checker
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.
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.
diffusion models color checkers illumination estimation single-step deterministic inference Laplacian decomposition mask-based data augmentation