Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings

TL;DR

This paper presents a hyperspectral image processing pipeline utilizing hyper-hue and EMAP descriptors to improve layer separation in old master drawings, aiding art analysis and authentication.

Contribution

It introduces a novel hyperspectral layer separation method using hyper-hue and EMAP, demonstrating superior performance over RGB-based techniques.

Findings

Hyperspectral images outperform RGB images in layer separation.

Spectral focus stacking enhances layer differentiation.

Hyper-hue and EMAP descriptors improve analysis accuracy.

Abstract

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 In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP).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.