Low-Cost Multispectral System Design for Pigment Analysis in Works of Art

Abstract

To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need.

Non-Destructive Investigation of Paintings on Different Substrates. Methodology, Achievements and Limitations.

“…A latest trend in the non-destructive research is the coupling of hyperspectral imaging with motorized XRF scanning easels. In such cases, mapping data are collected by XRF at the same time and from the same area as images by the hyperspectral camera, thus reducing the overall examination time of the artefact compared to when the methods are applied separately [Kleynhans et al, 2021;Gabrieli et al, 2021]. However, this could be exploited only by museums that already possess such instrumentation and cannot be easily supported by cultural heritage institutes due to the high budget, the requirement of specialized equipment and the conditions of maximum accuracy.…”

Robust seed germination prediction using deep learning and RGB image data

“…None of these efforts, however, has ripened to an operational tool, probably due to insufficient efficacy, reproducibility or robustness needed for real application. The high costs of high-resolution spectral detectors may also pose a market entry barrier for such products and inhibit their development 31 .…”