Improvement of Spectral Imaging by Pigment Mapping

Spectral imaging has been widely developed over the last ten years for archiving cultural heritage. It can retrieve spectral reflectance of each scene pixel and provide the possibility to render images for any viewing condition. A new spectral reconstruction method, the matrix R method, can achieve high spectral and colorimetric accuracies simultaneously for a specific viewing condition. Although the matrix R method is very effective, the reconstructed reflectance spectrum is not smooth when compared with in situ spectrophotometry. The goal of this research was to smooth the spectrum and make it more accurate. One possible solution is to identify pigments and find their compositions for each pixel. After that, the reflectance spectrum can be modified based on two-constant Kubelka-Munk theory using the absorption and scattering coefficients of these pigments, weighted by their concentrations. The concentrations were optimized to best fit the spectral reflectance predicted by the matrix R method. As a preliminary experiment, it was assumed that a custom target was painted using several known pigments. The simulation results show that incorporating pigment mapping into the matrix R method can recover the smoothness of the reflectance spectrum, and further improve spectral accuracy of spectral imaging. Introduction Traditional colorimetric devices acquire only three samples, critically under-sampling spectral information and suffering from metamerism. Alternatively, spectral devices increase the number of samples and can reconstruct spectral information for each scene pixel. Retrieved spectral information can be used to render color images for any viewing condition. Spectral imaging has been widely developed over the last ten years for archiving culture heritage at a number of institutes worldwide. Three spectral acquisition systems have been developed and tested in our laboratory. Recently, the matrix R method was proposed and implemented for spectral imaging reconstruction. The method followed the Wyszecki hypothesis where a spectrum can be decomposed into a fundamental stimulus and a metameric black. The spectral reflectance and tristimulus values were both calculated from multichannel camera signals. Then the hybrid spectral reflectance was generated by combing the fundamental stimulus and metameric black predicted from tristimulus values and spectral reflectance, respectively. This method achieved high spectral and colorimetric accuracies simultaneously for a certain viewing condition. The spectral accuracy of this method was mainly determined by the estimated spectral reflectance, which was calculated by multiplying the multi-channel camera signals with a transformation matrix. Each column of the transformation matrix can be estimated by a basis vector, and spectral reflectance can be represented as a linear combination of these basis vectors, weighted by the multi-channel camera signals. A transformation matrix for a six-channel virtual camera is shown in Figure 1. Due to the wavelike shape of the basis vectors, the predicted spectral reflectance for a white patch, for examples, is not as flat as in situ spectrophotometry, shown in Figure 2. The goal of this research was to smooth reflectance spectra and to further improve spectral accuracy. 360 460 560 660 760 −300 −200 −100 0 100 200 300 Wavelength (nm) B as is V ec to r Figure 1. The transformation matrix from six-channel camera signals to spectral reflectance factor. 360 460 560 660 760 0 0.2 0.4 0.6 0.8 Wavelength (nm) R ef le ct an ce F ac to r Figure 2. Measured (solid) and predicted (dashed) spectral reflectance factors