CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera
  Color Constancy
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.
6801eaa05246a16677d1f2d9
Dongyoung Kim
claimed_verified
2025-04-18T09:38:39.520Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/645dcc0da19f3e64bbf36492/SPGOweEnV5syFFpf40niQ.png
Dongyoung Kim
user
dongyong2
6801eaa05246a16677d1f2da
Mahmoud Afifi
claimed_verified
2025-05-16T07:13:03.166Z
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Mahmoud Afifi
user
mafifi
6801eaa05246a16677d1f2db
Dongyun Kim
6801eaa05246a16677d1f2dc
Michael S. Brown
6801eaa05246a16677d1f2dd
Seon Joo Kim
CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera
  Color Constancy
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.
A learning-based method for cross-camera color constancy uses pre-calibrated color correction matrices to create a camera fingerprint embedding, achieving state-of-the-art performance with limited data and without retraining.
white balancing image signal processor ISP color casts raw color space cross-camera color constancy color correction matrices CCMs Planckian locus camera fingerprint embedding CFE data augmentation interpolation