CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera
  Color Constancy
Authors: name: Dongyoung Kim, name: Mahmoud Afifi, name: Dongyun Kim, name: Michael S. Brown, name: Seon Joo Kim

Computational color constancy, or white balancing, is a key module in a
camera's image signal processor (ISP) that corrects color casts from scene
lighting. Because this operation occurs in the camera-specific raw color space,
white balance algorithms must adapt to different cameras. This paper introduces
a learning-based method for cross-camera color constancy that generalizes to
new cameras without retraining. Our method leverages pre-calibrated color
correction matrices (CCMs) available on ISPs that map the camera's raw color
space to a standard space (e.g., CIE XYZ). Our method uses these CCMs to
transform predefined illumination colors (i.e., along the Planckian locus) into
the test camera's raw space. The mapped illuminants are encoded into a compact
camera fingerprint embedding (CFE) that enables the network to adapt to unseen
cameras. To prevent overfitting due to limited cameras and CCMs during
training, we introduce a data augmentation technique that interpolates between
cameras and their CCMs. Experimental results across multiple datasets and
backbones show that our method achieves state-of-the-art cross-camera color
constancy while remaining lightweight and relying only on data readily
available in camera ISPs.