Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction

2026-07-13

AI Summary

This work addresses color correction inaccuracies in digital cameras under complex spectral and high-chromaticity LED illumination, which arise from the nonlinear relationship between sensor responses and the CIE XYZ color space. To mitigate this, the authors propose an illumination-adaptive 3D lookup table framework, termed C²LUT, that integrates chromaticity-aware illumination representation with nonlinear color transformation. The method employs Tucker tensor decomposition to compress the lookup table, achieving a favorable trade-off between colorimetric accuracy and hardware deployment efficiency. Evaluated on a large-scale dataset comprising 1,473 spectral illuminants, C²LUT demonstrates consistent performance gains across multiple cameras, diverse lighting conditions, and real-world imagery—reducing CIE ΔE₀₀ error by up to 20% and angular error by as much as 18%, while adhering to the computational constraints of modern image signal processors (ISPs).

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.

Problem

• color correction
• illuminant adaptation
• camera ISP
• spectrally complex illuminants
• non-linear color transformation

Innovation

• Illuminant-adaptive
• 3D Lookup Tables
• Color Correction
• Tucker Tensor Decomposition
• Spectral Power Distribution