An investigation of multispectral imaging for the mapping of pigments in paintings

Abstract

Compared with colorimetric imaging, multispectral imaging has the advantage of retrieving spectral reflectance factor of each pixel of a painting. Using this spectral information, pigment mapping is concerned with decomposing the spectrum into its constituent pigments and their relative contributions. The output of pigment mapping is a series of spatial concentration maps of the pigments comprising the painting. This approach was used to study Vincent van Gogh's The Starry Night. The artist's palette was approximated using ten oil pigments, selected from a large database of pigments used in oil paintings and a priori analytical research on one of his self portraits, executed during the same time period. The pigment mapping was based on single-constant Kubelka-Munk theory. It was found that the region of blue sky where the stars were located contained, predominantly, ultramarine blue while the swirling sky and region surrounding the moon contained, predominantly, cobalt blue. Emerald green, used in light bluish-green brushstrokes surrounding the moon, was not used to create the dark green in the cypresses. A measurement of lead white from Georges Seurat's La Grande Jatte was used as the white when mapping The Starry Night. The absorption and scattering properties of this white were replaced with a modern dispersion of lead white in linseed oil and used to simulate the painting's appearance before the natural darkening and yellowing of lead white oil paint. Pigment mapping based on spectral imaging was found to be a viable and practical approach for analyzing pigment composition, providing new insight into an artist's working method, the possibility for aiding in restorative inpainting, and lighting design.

Comparing the effectiveness of hyperspectral imaging and Raman spectroscopy: a case study on Armenian manuscripts

“…In such a study a pixel may be several metres across and cover both a patch of grass and some tarmac, whereas for paint materials this is not the case, as pigments are uniformly dispersed in the binding media and the spectral response is not a simple linear mixture of the reflectance. The Kubelka-Munk (KM) theory is more appropriate [1,9] and is used in the paint industry to calculate the ratios of paints to match a given colour [1]. The effect of binding medium, particle size, and concentration have been systematically studied and dirt and varnish can also have an effect [1].…”

“…d13. For the purpose taking this photo of the equipment the room lights have been left on, during a scan the room lights are always off 9 …”

“…Scientific techniques have been applied to pigment identification as part of conservation since the late twentieth century [1,2] aiding: characterisation of the palette of an artist or workshop [3,4]; art historical understanding of the artist [5]; identification of restorations or interventions [3,6]; monitoring degradation [7]; detailing the conservation state of an item [3]; revealing preparatory sketches [6], underdrawings [3], or palimpsests [8]; segmentation of a painting into regions for differential processing such as colour retouching [9]. Such knowledge is of value to researchers and can also aid in effective conservation strategies and restoration [7], and answer questions of provenance [3,4,6,7,[10][11][12].…”

“…White calibration is carried out using a piece of Spectralon ® , 9 and dark calibration is performed by covering the lens with its lens cap. The room lights are off during calibration and scanning as the fluorescent lights produce unwanted lines in the scan, ideally the room would be in darkness so that any fluctuations in light intensity and thus reflectance values are eliminated.…”

Advances in multispectral and hyperspectral imaging for archaeology and art conservation

“…This is sometimes referred to as the single constant KM theory. Since the aim here is to identify the pigments in a mixture, the concentrations of the single pigments were set as free parameters to find the best non-negative least squared fit of the predicted spectrum to the actual measured spectrum of the mixture for each combination of reference pigments [67] [79]. Liang et al [67] showed that the method worked even for pigment mixtures without the addition of white pigments, except in cases where pigments with high absorption or very low scattering are involved.…”