CCMNet：利用校准色彩校正矩阵实现跨相机色彩恒常性

CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy

April 10, 2025

作者: Dongyoung Kim, Mahmoud Afifi, Dongyun Kim, Michael S. Brown, Seon Joo Kim

cs.AI

摘要

计算色彩恒常性，或称白平衡，是相机图像信号处理器（ISP）中的关键模块，用于校正由场景光照引起的色偏。由于这一操作在相机特定的原始色彩空间中进行，白平衡算法必须适应不同的相机。本文提出了一种基于学习的跨相机色彩恒常性方法，该方法无需重新训练即可泛化至新相机。我们的方法利用了ISP上预先校准的色彩校正矩阵（CCMs），这些矩阵将相机的原始色彩空间映射至标准空间（如CIE XYZ）。我们利用这些CCMs将预定义的照明颜色（即沿普朗克轨迹）转换到测试相机的原始空间。映射后的光源被编码为紧凑的相机指纹嵌入（CFE），使网络能够适应未见过的相机。为了防止训练过程中因相机和CCMs数量有限导致的过拟合，我们引入了一种数据增强技术，该技术在相机及其CCMs之间进行插值。跨多个数据集和骨干网络的实验结果表明，我们的方法在实现最先进的跨相机色彩恒常性的同时，保持了轻量级，并且仅依赖于相机ISP中现成的数据。

English

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.