Spectral reflectance curves for multispectral imaging, combining different techniques and a neural network

In this paper. we present an alternative procedure for the digital reconstruction of spectral reflectanc Curves of oil painting on canvas using multispectral imaging. The technique is based on a combination of the results obtained by pseudo-inverse, principal component analysis and interpolation; these results are the input to a feed-forward back propagation neural network fittin the values of the curves to a target obtained using a spectrophotometer Shimadzu UV2401. Goodness-of-Fit Coefficien (GFC), absolute mean error (ABE) and spectral Root Mean Squared error (RMS) are the metrics used to evaluate the performance of the procedure proposed.