Surfing Virtual Waves to Thermal Tomography: From model- to deep learning-based reconstructions

Kovacs P, Lehner B, Thummerer G, Mayr G, Burgholzer P, Huemer M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Signal Processing Magazine Institute of Electrical and Electronic Engineers

Book Volume: 39

Pages Range: 55-67

Journal Issue: 1

DOI: 10.1109/MSP.2021.3120978

Abstract

Thermographic imaging is a fast and contactless way of inspecting material parts. Usually, with model-driven evaluation procedures, lateral heat flow is ignored, and, thus, 1D reconstruction is applied to detect defects. However, to correctly size defects, the lateral heat flow must be considered, which requires a full 3D reconstruction. The 3D thermal defect imaging is a major challenge because heat propagation is an irreversible process. The virtual wave concept (VWC) is a recently developed method that considers both lateral and axial heat flows and, therefore, allows multidimensional reconstruction at improved spatial resolution. This approach decomposes the problem into two steps; can be used for 1D, 2D, and 3D heat conduction problems; and provides new alternatives to using physical priors (e.g., nonnegativity and/or sparsity), all of which improve reconstruction accuracy at a relatively low computational cost.

Involved external institutions

Technische Universität Wien / Vienna University of Technology AT Austria (AT) Fachhochschule Oberösterreich (FH OÖ) AT Austria (AT) Eötvös Loránd University / Eötvös Loránd Tudományegyetem (ELTE) HU Hungary (HU) Upper Austrian Research GmbH AT Austria (AT) Silicon Austria Labs AT Austria (AT)

How to cite

APA:

Kovacs, P., Lehner, B., Thummerer, G., Mayr, G., Burgholzer, P., & Huemer, M. (2022). Surfing Virtual Waves to Thermal Tomography: From model- to deep learning-based reconstructions. IEEE Signal Processing Magazine, 39(1), 55-67. https://dx.doi.org/10.1109/MSP.2021.3120978

MLA:

Kovacs, Peter, et al. "Surfing Virtual Waves to Thermal Tomography: From model- to deep learning-based reconstructions." IEEE Signal Processing Magazine 39.1 (2022): 55-67.

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