用预标定的色彩矩阵实现跨相机自动白平衡，无需重新训练。
CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy
- 基于预标定的色彩校正矩阵，将标准光源映射到测试相机原始空间。
- 通过紧凑的相机指纹嵌入，使模型在未见相机上仍能准确白平衡。
- 仅依赖相机内置数据，轻量且适用于真实设备部署。
计算白平衡是摄像头图像信号处理器（ISP）中的关键模块，用于修正场景光照引起的色偏。由于该操作在相机特有的原始色彩空间中进行，白平衡算法需适配不同相机。本文提出一种基于学习的跨相机白平衡方法，可在不重新训练的情况下泛化至新相机。该方法利用ISP中已预标定的色彩校正矩阵（CCMs），将标准光源（沿普朗克轨迹）映射至测试相机的原始色彩空间。这些映射后的光源被编码为紧凑的相机指纹嵌入（CFE），使网络可适应未见过的相机。为防止因训练相机和CCM数量有限导致过拟合，我们引入一种数据增强策略，对相机及其CCM进行插值。多数据集与骨干网络实验表明，该方法在跨相机白平衡任务中达到当前最优性能，同时保持轻量化，并仅依赖于摄像头ISP中现成的数据。
原文摘要 · Abstract (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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。