Hyperspectral imaging has become an increasingly used tool in the analysis of works of art. However, the quality of the acquired data and the processing of that data to produce accurate and reproducible spectral image cubes can be a challenge to many cultural heritage users. The calibration of data that is both spectrally and spatially accurate is an essential step in order to obtain useful and relevant results from hyperspectral imaging. Data that is too noisy or inaccurate will produce sub-optimal results when used for pigment mapping, the detection of hidden features, change detection or for quantitative spectral documentation. To help address this, therefore, we will examine the specific acquisition and calibration workflows necessary for works of art. These workflows include the key parameters that must be addressed during acquisition and the essential steps and issues at each of the stages required during post-processing in order to fully calibrate hyperspectral data. In addition, we will look in detail at the key issues that affect data quality and propose practical solutions that can make significant differences to overall hyperspectral image quality.

Terahertz time-domain imaging (THz-TDI) and spectral-domain optical coherence tomography (SD-OCT) are two techniques capable of providing 3D datasets from which depth profiles and cross-sectional images of an object can be derived. They are novel photonics technologies of particular relevance to the field of heritage science, for which the comprehension of the stratigraphic structure of a cultural heritage object may help in the understanding of its artistic technology and state of preservation. The differences in imaging depth, field of view, and axial/lateral resolutions of the two imaging techniques provide different but complementary information of the same scene. Through the use of multimodal image fusion, the user will benefit from access to a detailed comparison of the information content of the two different datasets and images. To carry this out, we have developed a first data processing chain with the aim of representing THz-TDI and SD-OCT cross-sectional images in a unique image grid and space, in which the different datasets are dimensionally consistent.

In a study of multispectral and hyperspectral reflectance imaging, a Round Robin Test assessed the performance of different systems for the spectral digitisation of artworks. A Russian icon, mass-produced in Moscow in 1899, was digitised by ten institutions around Europe. The image quality was assessed by observers, and the reflectance spectra at selected points were reconstructed to characterise the icon’s colourants and to obtain a quantitative estimate of accuracy. The differing spatial resolutions of the systems affected their ability to resolve fine details in the printed pattern. There was a surprisingly wide variation in the quality of imagery, caused by unwanted reflections from both glossy painted and metallic gold areas of the icon’s surface. Specular reflection also degraded the accuracy of the reconstructed reflectance spectrum in some places, indicating the importance of control over the illumination geometry. Some devices that gave excellent results for matte colour charts proved to have poor performance for this demanding test object. There is a need for adoption of standards for digitising cultural heritage objects to achieve greater consistency of system performance and image quality.

A description of multispectral image capture is presented from a signal processing point of view. Indeed, this article is based on an equation that models the multispectral acquisition of images. The main components of this equation are described. They correspond to the lighting conditions, the filters, the sensor sensitivity, and associated noise sources. Moreover, the optimization problems involved in the design of multispectral cameras, their calibration, and the processing of the obtained data are introduced within the same mathematical framework.