Pigment identification and mapping gives us insight into an artists' material use, allows us to measure slow chemical changes in painted surfaces, and allows us to detect anachronistic uses of materials that can be associated with either forgeries or past restorations. Earlier work has demonstrated the potential of a dictionary-based reflectance approach for pigment classification. This technique identifies pigments by searching for the pigment combinations that best reproduce the measured reflectance curve. The prospect of pigment classification through modeling is attractive because it can be extended to a layered medium -- potentially opening a route to a depth-resolved pigment classification method. In this work, we investigate a layered pigment classification technique with a fused deep learning and optimization-based Kubelka-Munk framework. First, we discuss the efficacy of the algorithm in a thick, single-layer system. Specifically, we consider the impacts of layer thickness, total pigment concentration, and spectrally similar pigment combinations. Following a thorough discussion of the single layer problem, the system is generalized to multiple layers. Finally, as a concrete example, we use the two-layered system to demonstrate both the impacts of layer thickness and dictionary content on paint localization within the painting. Results of the algorithm are then shown for mock-up paintings for which the ground truth is known.
The use of non-invasive hyperspectral imaging techniques has become standard practice in the materials analysis and study of precious cultural heritage objects such as drawings, paintings, murals and more. However, the non-linear mixing of spectral signatures from complex and heterogenous objects with multiple colorants present below the resolution limits of the camera can complicate material identification. Consequently, ground truth measurements are still usually obtained from microscopic samples removed and embedded to expose stratigraphy and obtain sub-surface information about the artist’s material choices and technique. This work considers a microscopic spectral imaging technique capable of mapping molecular information in such micro samples at high spatial and spectral resolution while avoiding some of the challenges of complimentary techniques, such as swamping fluorescence in Raman spectroscopy or long integration times using FT-IR spectroscopy. Construction of a dark field hyperspectral microscope for cultural heritage samples is described using a tunable light source to illuminate the sample monochromatically from the visible to near infrared wavelengths, with the diffusely reflected light collected from the specimen with a long working distance, 20x objective. The illumination and detection arms were decoupled to better focus the power of the tunable light source across the tunable range through Köhler illumination optics. By mounting the optical train on a rotating arm, we can achieve multiple angles of illumination and optimize lighting conditions. The sample is also rotated in order to reconstruct an even distribution of light across the field of view. This multi-axis movement capability also provides exciting opportunities to leverage more than simple spectral information from an image series such as surface topography and differential phase contrast information. The developed microscope was used create a library of spectral signatures for comparison to painting cross sections, and the ability of the microscope to identify and examine individual pigment particles was tested.
KEYWORDS: Optical coherence tomography, Mirrors, Sensors, Signal to noise ratio, Imaging systems, Data acquisition, Image resolution, Cultural heritage, Reflectivity, Control systems
Accurate measurements of the geometric shape and the internal structure of cultural artifacts are of great importance for the analysis and understanding of artworks such as paintings. Often their complex layers, delicate materials, high value and uniqueness preclude all but the sparsest sample-based measurements (microtomy or embedding of small chips of paint). In the last decade, optical coherence tomography (OCT) has enabled dense point-wise measurements of layered surfaces to create 3D images with axial resolutions at micron scales. Commercial OCT systems at biologically-useful wavelengths (900 nm to 1.3 μm) can reveal some painting layers, strong scattering and absorption at these wavelengths severely limits the penetration depth. While Fourierdomain methods increase measurement speed and eliminate moving parts, they also reduce signal-to-noise ratios and increase equipment costs. In this paper, we present an improved lower-cost time-domain OCT (TD-OCT) system for deeper, high-resolution 3D imaging of painting layers. Assembled entirely from recently-available commercially-made parts, its 2x2 fused fiber-optic coupler forms an interferometer without a delicate, manuallyaligned beam-splitter, its low-cost broadband Q-switched super-continuum laser source supplies 20 KHz 0.4-2.4 μm coherent pulses that penetrate deeply into the sample matrix, and its single low-cost InGaAs amplified photodetector replaces the sensitive spectroscopic camera required by Fourier domain OCT (FD-OCT) systems. Our fiber and filter choices operate at 2.0±0.2 μm wavelengths, as these may later help us characterize scattering and absorption characteristics, and yield axial resolution of about 4.85 μm, surprisingly close to the theoretical maximum of 4.41 μm. We show that despite the moving parts that make TD-OCT measurements more timeconsuming, replacing the spectroscopic camera required by FD-OCT with a single-pixel detector offers strong advantages. This detector measures interference power at all wavelengths simultaneously, but at a single depth, enabling the system to reach its axial resolution limits by simply using more time to acquire more samples per Ascan. We characterize the system performance using material samples that match real works of art. Our system provides an economical and practical way to improve 3D imaging performance for cultural heritage applications in terms of penetration, resolution, and dynamic range.
A current focus of art conservation research seeks to accurately identify materials, such as oil paints or pigments, used in a work of art. Since many of these materials are fluorescent, measuring the fluorescence lifetime following an excitation pulse is a useful non-contact, quantitative method to identify pigments. In this project, we propose a simple method using a dynamic vision sensor to efficiently characterize the fluorescence lifetime of a common pigment named Egyptian Blue, which is consistent with x-ray techniques. We believe our fast, compact and cost-effective method for fluorescence lifetime analysis is useful in art conservation research and potentially a broader range of applications in chemistry and materials science.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.