Predicting Pigment Color Degradation with Time Series Models

Ciortan, Irina Mihaela; Poulsson, Tina Grette; George, Sony; Hardeberg, Jon Yngve

doi:10.2352/cic.2022.30.1.44

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The deviation happens mostly in the region 600-650 nm, which might also indicate unexplained variation of the tensor decomposition model in this spectral range. In future, other approaches to model the fading rate can be implemented towards improvement, such as spline interpolation or time-series models [71].…”

Section: Future Modellingmentioning

confidence: 99%

Tensor decomposition for painting analysis. Part 1: pigment characterization

Ciortan

Poulsson²,

George

et al. 2023

Herit Sci

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Photo-sensitive materials tend to change with exposure to light. Often, this change is visible when it affects the reflectance of the material in the visible range of the electromagnetic spectrum. In order to understand the photo-degradation mechanisms and their impact on fugitive materials, high-end scientific analysis is required. In a two-part article, we present a multi-modal approach to model fading effects in the spectral, temporal (first part) and spatial dimensions (second part). Specifically, we collect data from the same artwork, namely “A Japanese Lantern” by Norwegian artist, Oda Krohg, with two techniques, point-based microfading spectroscopy and hyperspectral imaging. In this first part, we focus on characterizing the pigments in the painting based on their spectral and fading characteristics. To begin with, using microfading data of a region in the painting, we analyze the color deterioration of the measured points. Then, we train a tensor decomposition model to reduce the measured materials to a spectral basis of unmixed pigments and, at the same time, to recover the fading rate of these endmembers (i.e. pure, unmixed chemical signals). Afterwards, we apply linear regression to predict the fading rate in the future. We validate the quality of these predictions by spectrally comparing them with temporal observations not included in the training part. Furthermore, we statistically assess the goodness of our model in explaining new data, collected from another region of the painting. Finally, we propose a visual way to explore the artist’s palette, where potential matches between endmembers and reference spectral libraries can be evaluated based on three metrics at once.

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Section: Future Modellingmentioning

confidence: 99%

Tensor decomposition for painting analysis. Part 1: pigment characterization

Ciortan

Poulsson²,

George

et al. 2023

Herit Sci

Self Cite

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“…While in these works the fading experiments allowed for high dosages of light, this might not be possible when the analysis is performed on real artifacts. In these latter cases, the future change could be predicted from the set of measured data using linear regression [9] or time-series models [25].…”

Section: Mapping Of Photodegradationmentioning

confidence: 99%

Tensor decomposition for painting analysis. Part 2: spatio-temporal simulation

Ciortan

Poulsson²,

George

et al. 2023

Herit Sci

Self Cite

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In a previous article, we modelled the spectral and temporal dimensions of the photodegradation behaviour of pigments in the painting “A Japanese Lantern” by Oda Krohg. In particular, we extracted the endmembers and spectral fading rate of pigments by applying tensor decomposition on a time-series of spectroscopic point measurements. Now, we capture the same painting with a hyperspectral imaging setup and propose an approach to render the fading effects as 2D images. More precisely, from the hyperspectral image, we compute the concentration maps of each previously identified endmember with a least-squares unmixing method. Subsequently, by using tensor algebra, we multiply the concentration maps with the endmembers and their corresponding fading rate and obtain a 4D tensor where each pixel in the image is described by a spectrum and a fading function. This way, we generate past and future spatio-temporal simulations of the painting’s appearance by reversing and elevating light exposure, respectively.

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Physico‐chemical characterisation and light stability of dyes and pigments found in cultural heritage objects: Insights from microfading testing for assessing light fastness

del Hoyo‐Meléndez

2024

Coloration Technology

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Understanding the chemistry of dyes and pigments found in cultural heritage objects and their permanence is central for their preservation. Heritage science research has generally focused on either identification of materials present on actual objects or accelerated and natural ageing of mock‐up samples prepared using historically accurate methods to simulate the materiality of cultural heritage objects. A more recent strategy is the integration of these two research areas, which provides a holistic approach to assess both the chemical composition and stability of materials. Over the last 30 years, microfading testing (MFT) has notably contributed to understanding materials' responsiveness to light, minimising damage to objects from museum lighting and revealing insights into molecular structures of dyes and pigments, when employed in conjunction with other techniques. By combining MFT with diverse analytical methods, including imaging, spectroscopy, microscopy and chromatography, a more comprehensive approach is achieved. This joined‐up strategy contributes to improved decision‐making processes in the conservation and preservation of cultural heritage objects.

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Predicting Pigment Color Degradation with Time Series Models

Cited by 3 publications

References 12 publications

Tensor decomposition for painting analysis. Part 1: pigment characterization

Tensor decomposition for painting analysis. Part 1: pigment characterization

Tensor decomposition for painting analysis. Part 2: spatio-temporal simulation

Physico‐chemical characterisation and light stability of dyes and pigments found in cultural heritage objects: Insights from microfading testing for assessing light fastness

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