Physics-Driven Computational Multispectral Imaging for Accurate Color Measurement.

Accurate color measurement is crucial for ensuring reliable sensing performance in vision-based applications. However, existing color measurement methods suffer from illumination variability, operational complexity, and perceptual subjectivity. In this study, dental color measurement, with its strict perceptual and spectral fidelity demands, is adopted to validate the proposed method. Using self-made resin-permeated ceramic teeth, this study proposes a deep-learned end-to-end spectral reflectance prediction framework to achieve snapshot teeth spectral reflectance from RGB images under complex light sources in the fundamental spectral domain through the construction of a physically interpretable network that enables physically informed feature fusion. A dual-attention modular-information fusion neural network is developed to recover the spectral reflectance directly from the RGB image for natural teeth and ceramics across multiple scenarios. A dataset containing 4000 RGB-hyperspectral image pairs is built from a self-designed optical system with complex illumination conditions. Results confirm that the proposed framework demonstrates effective performance in predicting teeth spectral reflectance with an MSE of 0.0024 and an SSIM of 0.8724. This method achieves high-accuracy color measurement while avoiding the color mismatch caused by metamerism, which empowers various advanced applications including optical property characterization, 3D surface reconstruction, and computer-aided restorative design.