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Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

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A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.
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Document Type: Research Article

Affiliations: 1: Graduate School of Science and Engineering, Chiba University, Chiba, Japan 2: Munsell Color Science Laboratory, Program of Color Science, Rochester Institute of Technology, Rochester, New York, USA

Publication date: May 1, 2020

This article was made available online on March 16, 2020 as a Fast Track article with title: "Estimating Pigment Concentrations from Spectral Images Using an Encoder–Decoder Neural Network".

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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