Hyperspectral Imaging Analyses of Cleaning Tests on Edvard Munch's Monumental Aula Paintings

Abstract

The development of innovative applications of imaging technologies for monitoring change in cultural heritage objects is central to the CHANGE-ITN project. Within this framework, the authors’ ongoing work targets the harnessing of hyperspectral imaging (HSI) for the documentation of cleaning treatments on the monumental University of Oslo Aula unvarnished oil paintings on canvas (1909–1916) by Edvard Munch (1863–1944). This is applicable to unvarnished paint surfaces in general. Particularly, this paper evaluates HSI techniques for the investigation of cleaning tests carried out in 2008, which targeted the removal of visible, ingrained particulate matter from Munch's unvarnished oil paints and exposed grounds on Kjemi (1914–1916). An exploratory in situ HSI campaign using visible to near-infrared (VNIR) and short-wave infrared (SWIR) cameras was undertaken to capture the soiled or previously cleaned areas on the painting's surface, with comparisons to analogous mock-ups. Principal component analysis (PCA) was used to extract information about possible trends with respect to the soiling. Results indicate that in both VNIR and SWIR ranges, the intensity of reflectance can be used to discriminate statistically between soiled and unsoiled/cleaned areas, whereas in SWIR, combination and first overtone bands of oil peaks can also serve as markers.