Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
This work addresses the challenge for art historians and restorers in analyzing layered artistic materials, though it appears incremental as it builds on existing hyperspectral imaging techniques with specific feature improvements.
The authors tackled the problem of separating overlapping material layers in old master drawings by developing an image processing pipeline using hyperspectral images, demonstrating that hyperspectral data enables better layer separation than RGB images and that spectral focus stacking improves results.
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. Using this pipeline, we show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking aids the layer separation. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). Our comparative results with other features underline the efficacy of the three proposed improvements.