Identification of pictorial materials by means of optimized multispectral reflectance image processing

Image spectroscopy may allow identifying the materials present on a painting surface in a non-invasive way. The proposed method aims at optimizing, and thus reducing, the number of filters employed, while still providing a robust method, that achieves similar performances as traditional ones, which in turn employ a large number of filters. Moreover, we targeted the identification of the pigments present on the outer layer of a painting independently from their thickness, the underlying background or support, the binder employed, their aging and acquisition set-up. In order to achieve this objective, a relevant number of swatches have been prepared, on different supports and with different thicknesses and binding mediums. Spectral reflectance curves of such chemically known pictorial layers have been recorded by means of a spectrometer and a spectrophotometer. A novel Principal Component Analysis (PCA) based approach has been devised to select the most relevant wavebands, i.e. those that allow the most effective discrimination among (quasi)metameric colours, which are thus not to be distinguished with the naked eye or with an RGB camera. Comparisons of results using the 13 filters available on the filter wheel and of a selection of only 3 and 4 filters, support the idea of the simplified version investigated in this paper being a viable alternative.