Abstract
Nondestructive inspection (NDI) has immensely contributed to the restoration of historic and artistic works. As one of the most common used NDI methods, active thermography is an easy-to-operate and efficient technique. Principal component thermography (PCT) has been widely used to deal with thermographic data for enhancing the visibility of subsurface defects. Unlike PCT, edge-group sparse PCT introduced herein enforces sparsity of principal component (PC) loadings by considering the spatial connectivity of thermographic image pixels. The feasibility and effectiveness of this method is illustrated by the experimental results of the defect characterization in an ancient marquetry sample with a fir wood support.
Highlights
In the restoration of historic and artistic works, nondestructive inspection (NDI) can help us with a good understanding of the state of antiques or finding the invisible details
Motivated by the fact that defects always behave as spatially connected pixel groups in the thermal images, an edge-group sparse Principal component thermography (PCT) (ESPCT) method is utilized for thermographic data analysis to Proceedings 2019, 27, 34; doi:10.3390/proceedings2019027034
Is an edge-group sparse penalty (ES-penalty) leading to a sparse loading vector whose nonzero elements are determined based on some important edges in G, where the importance of each edge is quantified with the weight (w, ) on it
Summary
In the restoration of historic and artistic works, nondestructive inspection (NDI) can help us with a good understanding of the state of antiques or finding the invisible details. As one of the commonly used NDI methods, active thermography is an imaging procedure [1], which is based on the analysis of heat flow induced by an energetic excitation of a test object. Active thermography has the characteristics of detection in large areas, recording in real time, and being easy to operate Despite these advantages, objects can become indistinguishable in the thermal images because of the existence of inhomogeneous backgrounds. Objects can become indistinguishable in the thermal images because of the existence of inhomogeneous backgrounds To solve this problem, plenty of thermographic data analysis methods have been developed. Motivated by the fact that defects always behave as spatially connected pixel groups in the thermal images, an edge-group sparse PCT (ESPCT) method is utilized for thermographic data analysis to Proceedings 2019, 27, 34; doi:10.3390/proceedings2019027034 www.mdpi.com/journal/proceedings. The defects can be revealed more clearly
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