Improved and Robust Spectral Reflectance Estimation

Accurately estimating spectral reflectance functions from color camera images is a hot research subject that demonstrates tremendous potential for illuminating engineering and computer vision applications. However, the impact of the illumination spectrum and camera responsivity (system functions) on estimation accuracy has not been systematically studied so far, nor has the impact of a “training” spectral reflectance set. In this study, a dual imaging reflectance optimization system is used based on a neural network and optimal system functions that are respectively trained and optimized using several sample sets. Simulations showed that such optimal systems, trained and optimized with the IES TM-30 spectral reflectance set, can have a substantially higher estimation accuracy compared to “real” systems composed of commercially available projector spectra and camera responsivities and that they are sufficiently robust under small changes in system function peak wavelength and spectral width due to changes in working temperature or with passing time. An analysis of the impact of the specific sample set database adopted for neural net training on estimation accuracy showed that training with the IES set results in good and stable performance, even for other sample sets and different illumination spectra. Training with the spectrally uniform IES spectral reflectance set is therefore advised for general-purpose, high-accuracy reflectance estimation systems. A comparison with a state-of-the-art method shows that the proposed method has a higher color prediction accuracy and a significantly shorter running time for realistic images with high resolution.