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Micromachines
Volume 13
Issue 1
10.3390/mi13010094
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Article

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

by
Xiaozhen Ren
1,*,
Yanwen Bai
2,
Yingying Niu
2,* and
Yuying Jiang
1,2
1
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
2
School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
Micromachines 2022, 13(1), 94; https://doi.org/10.3390/mi13010094
Submission received: 14 November 2021 / Revised: 24 December 2021 / Accepted: 28 December 2021 / Published: 8 January 2022
(This article belongs to the Section E: Engineering and Technology)
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Abstract

In order to solve the problems of long-term image acquisition time and massive data processing in a terahertz time domain spectroscopy imaging system, a novel fast terahertz imaging model, combined with group sparsity and nonlocal self-similarity (GSNS), is proposed in this paper. In GSNS, the structure similarity and sparsity of image patches in both two-dimensional and three-dimensional space are utilized to obtain high-quality terahertz images. It has the advantages of detail clarity and edge preservation. Furthermore, to overcome the high computational costs of matrix inversion in traditional split Bregman iteration, an acceleration scheme based on conjugate gradient method is proposed to solve the terahertz imaging model more efficiently. Experiments results demonstrate that the proposed approach can lead to better terahertz image reconstruction performance at low sampling rates.
Keywords: terahertz imaging; group sparsity; nonlocal self-similarity; conjugate gradient; acceleration scheme terahertz imaging; group sparsity; nonlocal self-similarity; conjugate gradient; acceleration scheme

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MDPI and ACS Style

Ren, X.; Bai, Y.; Niu, Y.; Jiang, Y. Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity. Micromachines 2022, 13, 94. https://doi.org/10.3390/mi13010094

AMA Style

Ren X, Bai Y, Niu Y, Jiang Y. Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity. Micromachines. 2022; 13(1):94. https://doi.org/10.3390/mi13010094

Chicago/Turabian Style

Ren, Xiaozhen, Yanwen Bai, Yingying Niu, and Yuying Jiang. 2022. "Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity" Micromachines 13, no. 1: 94. https://doi.org/10.3390/mi13010094

APA Style

Ren, X., Bai, Y., Niu, Y., & Jiang, Y. (2022). Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity. Micromachines, 13(1), 94. https://doi.org/10.3390/mi13010094

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