CCMNet：利用校準色彩校正矩陣實現跨相機色彩恆常性

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

計算色彩恆常性，或稱白平衡，是相機圖像信號處理器（ISP）中的關鍵模組，用於校正場景照明造成的色偏。由於此操作在相機特定的原始色彩空間中進行，白平衡算法必須適應不同的相機。本文介紹了一種基於學習的跨相機色彩恆常性方法，該方法能夠在不重新訓練的情況下泛化到新相機。我們的方法利用了ISP上預先校準的色彩校正矩陣（CCMs），這些矩陣將相機的原始色彩空間映射到標準空間（例如，CIE XYZ）。我們的方法使用這些CCMs將預定義的照明顏色（即沿普朗克軌跡）轉換到測試相機的原始空間中。映射後的照明顏色被編碼成一個緊湊的相機指紋嵌入（CFE），使網絡能夠適應未見過的相機。為了防止由於訓練期間相機和CCMs數量有限而導致的過擬合，我們引入了一種數據增強技術，該技術在相機及其CCMs之間進行插值。在多個數據集和骨幹網絡上的實驗結果表明，我們的方法在保持輕量級且僅依賴於相機ISP中現有數據的情況下，實現了最先進的跨相機色彩恆常性。

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.