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