Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction

• URL: http://arxiv.org/abs/2607.11681v2
• Date: Tue, 14 Jul 2026 09:02:52 GMT
• Title: Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction
• Abstract summary: We propose a color correction framework based on illuminant-adaptive three-dimensional lookup tables (LUTs)<n>Our method combines a chromaticity-aware illuminant representation with a non-linear color transformation, enabling accurate correction under illuminants.<n> Experiments across multiple cameras, illuminants, reflectance datasets, and real captured images demonstrate consistent improvements over existing methods for color correction.
• Score: 10.971431168902166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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