Issue |
J. Eur. Opt. Society-Rapid Publ.
Volume 20, Number 1, 2024
THz imaging
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/jeos/2024001 | |
Published online | 08 March 2024 |
Research Article
Terahertz nondestructive stratigraphic reconstruction of paper stacks based on adaptive sparse deconvolution
1
Georgia Tech-CNRS IRL2958, Georgia Tech-Europe, 2 Rue Marconi, 57070 Metz, France
2
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA
* Corresponding author: david.citrin@ece.gatech.edu
Received:
14
September
2023
Accepted:
18
January
2024
Characterizing the number of sheets in a stack of paper typically involves mechanical separation of the individual sheets. Here, we explore an nondestructive method that can be applied to the intact paper stack. Namely, terahertz time-of-flight tomography, together with post signal-processing technique sparse deconvolution based on a two-step iterative shrinkage-thresholding algorithm (SD/TWIST), is employed to reconstruct the stratigraphy of stacks of sheets of paper with multilayered structure in a nondestructive and noncontact manner. The double-Gaussian mixture model (DGMM) is also incorporated to suppress dispersion in the reflected THz echoes. The effectiveness and accuracy of the proposed adaptive sparse-deconvolution method are verified experimentally and numerically. Compared with the commonly used frequency wavelet-domain deconvolution (FWDD) method and previous implementations of sparse deconvolution based on an iterative-shrinkage and thresholding algorithm (SD/IST), the proposed sparse-deconvolution approach can provide a clearer and rapid stratigraphic reconstruction of the paper stacks studied, while ensuring accurate thickness information for each paper sheet in the presence of noise, revealing the potential usage of real-time THz tomographic-image processing.
Key words: Terahertz time-domain spectroscopy / Paper handling / Super-resolution characterization / Dispersion / Sparse deconvolution
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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