Locally weighted model spectral reflectance reconstruction based on LED multi-illuminant

Chun Liang; Enliang Zhao; Long Ma

doi:10.1117/12.3034972

3 September 2024 Locally weighted model spectral reflectance reconstruction based on LED multi-illuminant

Chun Liang, Enliang Zhao, Long Ma

Author Affiliations +

Proceedings Volume 13227, 2024 AI Photonics Technology Symposium; 1322702 (2024) https://doi.org/10.1117/12.3034972
Event: 2024 AI Photonics Technology Symposium, 2024, Wuhan, China

Abstract

In this paper, we propose a multi-illuminant imaging system that captures the original smartphone camera responses under multiple LED light sources and reconstructs surface-spectral reflectance using locally weighted training samples. The method unfolds in three stages: (1) error calculation based on the captured camera response data to select locally optimal training samples for each test sample, (2) computation of a weighted coefficient matrix for the chosen locally optimal training samples, and (3) reconstructing surface-spectral reflectance employing wiener estimation, linear programming model, quadratic programming model, and different smartphone cameras. Experimental results demonstrate that the proposed methods significantly improve reconstruction accuracy, with the reconstructed goodness-of-fit coefficients consistently exceeding 0.9980, thereby confirming the reliability of the localized weighted model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chun Liang, Enliang Zhao, and Long Ma "Locally weighted model spectral reflectance reconstruction based on LED multi-illuminant", Proc. SPIE 13227, 2024 AI Photonics Technology Symposium, 1322702 (3 September 2024); https://doi.org/10.1117/12.3034972

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KEYWORDS

Reflectivity

Cameras

Light sources

RGB color model

Reconstruction algorithms

Matrices

Imaging systems

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