用于相机色彩校正的光源自适应三维查找表

中文摘要 AI 辅助

色彩校正是相机图像信号处理（ISP）管道的关键组成部分，包括光源折扣和将与设备相关的传感器响应映射到与设备无关的色彩空间（如CIE XYZ）。由于相机传感器响应与CIE XYZ色彩空间之间的非线性关系，以及高色度和光谱复杂的LED光源的增加，准确的色彩校正仍然具有挑战性。我们提出了一种基于光源自适应三维查找表（LUT）的色彩校正框架，称为色彩校正LUT（C$^2$LUT）。我们的方法将色度感知光源表示与非线性色彩变换相结合，能够在跨越广泛色度和光谱复杂性的光源下进行准确校正。我们采用塔克张量分解来表示LUT，确保计算需求足够低，以便在相机ISP中部署。此外，我们引入了一个包含1473个光谱功率分布的大规模光源数据集，具有不同的色度和光谱轮廓。跨多个相机、光源、反射率数据集和真实捕获图像的实验表明，与现有色彩校正方法相比有一致的改进，将CIE $\Delta E_{00}$降低了20%，角度误差降低了18%，同时与现代相机硬件约束兼容。代码和数据集可在该https URL获取。

英文摘要

Color correction is a key component of camera image signal processing (ISP) pipelines, encompassing illuminant discounting and colorimetric mapping of device-dependent sensor responses to device-independent color spaces, such as CIE XYZ. Despite extensive research, accurate color correction remains challenging due to the non-linear relationship between camera sensor responses and CIE XYZ color space, as well as to the increasing presence of highly chromatic and spectrally complex LED illuminants. We propose a color correction framework based on illuminant-adaptive three-dimensional lookup tables (LUTs), which we call Color Correction LUT (C$^2$LUT). Our method combines a chromaticity-aware illuminant representation with a non-linear color transformation, enabling accurate correction under illuminants spanning a wide range of chromaticities and spectral complexities. We employ Tucker tensor decomposition to represent the LUTs, ensuring that computational requirements remain sufficiently low for deployment in camera ISPs. In addition, we introduce a large-scale illuminants dataset comprising 1,473 spectral power distributions, with different chromaticities and spectral profiles. Experiments across multiple cameras, illuminants, reflectance datasets, and real captured images demonstrate consistent improvements over existing methods for color correction, reducing CIE $ΔE_{00}$ by up to 20% and angular error by up to 18% while remaining compatible with modern camera hardware constraints. Code and datasets are available at https://github.com/claudiom4sir/C2LUT.

发表机构

• University of Milano-Bicocca（米兰比可卡大学）

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