Terahertz superresolution stratigraphic characterization of multilayered structures using sparse deconvolution
Open accessAbstract
Terahertz sparse deconvolution based on an iterative shrinkage algorithm is presented in this study to characterize multilayered structures. With an upsampling approach, sparse deconvolution with superresolution is developed to overcome the time resolution limited by the sampling period in the measurement and increase the precision of the estimation of echo arrival times. A simple but effective time-domain model for describing the temporal pulse spreading due to the frequency-dependent loss is also designed and introduced into the algorithm, which greatly improves the performance of sparse deconvolution in processing time-varying pulses during the propagation of terahertz waves in materials. Numerical simulations and experimental measurements verify the algorithms and show that sparse deconvolution can be considered as an effective tool for terahertz nondestructive characterization of multilayered structures.
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THz pulses contain localized features in both time and frequency domains.
“Because THz pulses are inherently sparse [24], and they contain localized features in both time and frequency domains, wavelet transform is particularly advantageous for this purpose.”
From Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy (2022) · cites this paper as [24]
The Introduction (p. 2) describes an approximately single-cycle THz pulse and temporal echoes, supporting time localization. The paper discusses THz spectra and frequency-dependent loss (Introduction, p. 2; Sparse Deconvolution Considering Pulse Spreading, p. 4), but does not say the pulses have localized features in the frequency domain. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTHz pulses are inherently sparse.
“Because THz pulses are inherently sparse [24], and they contain localized features in both time and frequency domains, wavelet transform is particularly advantageous for this purpose.”
From Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy (2022) · cites this paper as [24]
The Introduction (p. 2) says that reflected signals from multilayered structures contain a limited number of echoes and that their impulse response functions have a sparse representation. This supports sparsity in that setting, but not the broader claim that THz pulses themselves are inherently sparse. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Cites
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Flagged first, then by citations
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Terahertz time-domain spectroscopy
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“Because THz pulses are inherently sparse [24], and they contain localized features in both time and frequency domains, wavelet transform is particularly advantageous for this purpose.”
Background
- Authors
- Dong, J., Wu, X., Locquet, A., & Citrin, D. S.
- Affiliations
- Georgia Tech-Europe (FR)
- Venue
- IEEE Transactions on Terahertz Science and Technology
- Published
- 2017-05
- Volume
- 7
- Issue
- 3
- Pages
- 260-267
- ISSN
- 2156-342X (ISSN-L)
- 2156-3446
- OpenAlex
- W2593494288
- Cited by
- 103
- References
- 18
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 13 · 2023: 17 · 2022: 8 · 2021: 13 · 2020: 12 · 2019: 15 · 2018: 4 · 2017: 2
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- IEEE Transactions on Terahertz Science and Technology (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Conseil Régional du Grand Est and the Fonds Européen de Développement Régional
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Terahertz technology and applications
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Keywords
- terahertz imaging, sparse deconvolution, super-resolution, multilayer structures, frequency-dependent losses, stratigraphic characterization
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