Multispectral and Hyperspectral Imaging of Art: Quality, Calibration and Visualization

Abstract

Multispectral and hyperspectral imaging can be powerful tools for analyzing and documenting works of art due to their ability to simultaneously capture both accurate spectral and spatial information. The data can be used for a wide range of diagnostic and analytical purposes, including materials identification, pigment mapping, the detection of hidden features or areas of lost material, for colorimetric analysis or for precise quantitative documentation. However, a number of technical challenges exist which have prevented multispectral and hyperspectral imaging from realizing their full potential and which have prevented the technologies from becoming more widely used and routine analytical tools. Both multispectral and hyperspectral imaging systems require careful and precise acquisition workflows in order to produce useful data. In addition, processing and calibration of the acquired data can be a challenge for many cultural heritage users. Hyperspectral imaging, in particular, can produce vast quantities of raw data that require complex processing and the ability to manage the large resulting volumes of data. Moreover, the final high resolution and multidimensional data that is produced can be difficult to use or to visualize. This thesis, therefore, seeks to address some of these issues and seeks to analyze and quantify potential problems and then propose tools, workflows and methodologies to resolve and mitigate them. The research presented here focuses on two main areas. The first research area concerns the quality of spectral data and how to measure, quantify and improve it. To do so, it is necessary to first establish exactly what spectral quality is and what methods can be used to quantify it. These methods are then applied to ascertain the levels of quality seen in data acquired under routine operating conditions with an evaluation of data from an extensive round-robin test of hyperspectral imaging systems. In order to improve the quality of spectral data, the various elements that contribute to and affect spectral quality within a system are then analyzed. Ac quisition and calibration pipelines are then defined for both multispectral and hyperspectral equipment with practical guidelines and workflows provided that aim to help users produce the best quality data possible. The second research area concerns the visualization of such data and examines ways to facilitate and make large and complex image data accessible online. For this, an architecture, visualization techniques and a full software platform are presented for the efficient distribution and visualization of high resolution multi-modal and multispectral or hyperspectral image data. This work is then extended in order to push the technology to the limits and to apply the techniques to the field of astronomy where image sizes are at their most extreme