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

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.