Since the mineral pigments on the surface of Chinese paintings are small in particle size and the pigment layer is too thin to cover the paper completely, their spectral mixing often shows nonlinearity. In this paper, aimed to accurately estimate the different types of pigments abundance on the surface of Chinese paintings, we proposed an improved single-constant Kubelka-Munk (KM) method for pigments unmixing algorithm. First, we selected Malachite, Azurite and Cinnabar pigments and mixed them in pairs in different abundances. They were added glue and then painted on the rice paper to produce the samples. The spectra of pure pigments and their mixed samples were acquired by the instrument of ASD FieldSpec 4. Second, we calculated the absorption and scattering ratio of the samples, pure pigments and rice paper. The rice paper and pure pigments were regarded as endmembers together for the pigment layer is too thin to cover the paper completely. The abundances were figured out base on the Fully Constrained Least Squares (FCLS) method endmember. Third, the abundances for different pigment endmember were re-calculated after removing the paper endmember. The experimental results show that the improved single-constant KM method has higher unmixing accuracy compared with the traditional spectral unmixing methods, and has good prospects for the application of unmixing composite pigments on the surface of Chinese paintings.
Some ancient murals were found to be repainted on the surface of the original murals to form multi-layer murals. The patterns on the original layer are of great significance for studying the social and cultural behavior in that time. The hyperspectral imaging covers the visible and near-infrared bands, which has advantages for the information extraction of multi-layer murals. Therefore, a method to study the transmission performance of hyperspectral imaging on multi-layer simulated mural samples is proposed. By making mural samples, the mineral pigment painted on the surface is covered with 0-11 different layers of lime water. Then the samples were collected with hyperspectral images, and the method of principal component analysis and band calculation were used to analyze the enhancement effect of the mural patterns covered by different layers of lime water. The results show that hyperspectral imaging has certain transmittance to the interior of the mural and can enhance internal pigment information. The research results can support the information extraction of multi-layer murals to some extent.
In the digital protection of the cultural relics, the identification of the pigment mixtures on the surface of the painting has been the research spot for many years. In this paper, as a hyperspectral unmixing algorithm, sub-space distance unmixing is introduced to solve the problem of recognition of pigments mixture in paintings. Firstly, some mixtures of different pigments are designed to measure their reflectance spectra using spectrometer. Moreover, the factors affecting the unmixing accuracy of pigments’ mixtures are discussed. The unmixing results of two cases with and without rice paper and its underlay as endmembers are compared. The experiment results show that the algorithm is able to unmixing the pigments effectively and the unmixing accuracy can be improved after considering the influence of spectra of the rich paper and the underlaying material.
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