Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network
M. Moradi (TU Delft - Structural Integrity & Composites)
R. Ghorbani (TU Delft - Pattern Recognition and Bioinformatics)
Stefano Sfarra (University of L'Aquila)
D.M.J. Tax (TU Delft - Pattern Recognition and Bioinformatics)
D. Zarouchas (TU Delft - Structural Integrity & Composites)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, a spatiotemporal deep neural network (STDNN) is utilized for defect identification in a mock-up reproducing an artwork, taking into account both the temporal and spatial perspectives of step-heating (SH) thermography. Finally, the outcomes are compared to those of other conventional algorithms.