用物理模型让不同相机拍出的颜色更一致，还省算力。
Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping
- 基于物理规律建模光照变化下的原始图像，实现跨设备颜色映射。
- 在NUS和BeyondRGB数据集上优于当前最好方法，颜色一致性更强。
- 无需配对数据或同步拍摄，适合实际多相机系统部署。
在现代设备中，实现多相机间一致的色彩还原对无缝图像融合和图像处理管线（ISP）兼容至关重要，但受传感器与光学系统差异影响，这一任务极具挑战。现有原始图像到原始图像转换方法存在适应光照变化能力差、计算开销高或需同时运行相机且视场重叠等限制。本文提出神经物理模型（NPM），一种轻量级、基于物理信息的方法，通过模拟特定光照条件下的原始图像来估计设备间变换关系。NPM能有效适应不同光照条件，可基于物理测量初始化，并支持有/无配对数据训练。在NUS和BeyondRGB等公开数据集上的实验表明，NPM优于近期最先进方法，在不同传感器与光学系统间保持了更强的色度一致性。
原文摘要 · Abstract (English)
Achieving consistent color reproduction across multiple cameras is essential for seamless image fusion and Image Processing Pipeline (ISP) compatibility in modern devices, but it is a challenging task due to variations in sensors and optics. Existing raw-to-raw conversion methods face limitations such as poor adaptability to changing illumination, high computational costs, or impractical requirements such as simultaneous camera operation and overlapping fields-of-view. We introduce the Neural Physical Model (NPM), a lightweight, physically-informed approach that simulates raw images under specified illumination to estimate transformations between devices. The NPM effectively adapts to varying illumination conditions, can be initialized with physical measurements, and supports training with or without paired data. Experiments on public datasets like NUS and BeyondRGB demonstrate that NPM outperforms recent state-of-the-art methods, providing robust chromatic consistency across different sensors and optical systems.
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