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

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 94
Author(s):  
Xiaozhen Ren ◽  
Yanwen Bai ◽  
Yingying Niu ◽  
Yuying Jiang
Keyword(s):  
Imaging System ◽  
Dimensional Space ◽  
Terahertz Imaging ◽  
Acquisition Time ◽  
Group Sparsity ◽  
Self Similarity ◽  
Split Bregman ◽  
Edge Preservation ◽  
Imaging Model ◽  
Reconstruction Performance

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.

scholarly journals Hybrid Sparsity Model for Fast Terahertz Imaging

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1181
Author(s):  
Xiaozhen Ren ◽  
Yanwen Bai ◽  
Yuying Jiang
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Terahertz Imaging ◽  
Acquisition Time ◽  
Iteration Algorithm ◽  
Terahertz Time Domain Spectroscopy ◽  
Self Similarity ◽  
Split Bregman ◽  
Imaging Model ◽  
Image Acquisition Time

In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self-similarity constraints in a unified statistical model. In HSM, a weighted exponentiation shift-invariant wavelet transform is introduced to enhance the sparsity of the terahertz image. Simultaneously, the nonlocal self-similarity by means of the three-dimensional sparsity in the transform domain is exploited to ensure high-quality terahertz image reconstruction. Finally, a new split Bregman-based iteration algorithm is developed to solve the terahertz imaging model more efficiently. Experiments are presented to verify the effectiveness of the proposed approach.

Some Quantification Problems in Parallel EELS Images

1990 ◽  
Vol 48 (2) ◽  
pp. 36-37
Author(s):  
G. Botton ◽  
G. L’Espérance ◽  
M.D. Ball ◽  
C.E. Gallerneault
Keyword(s):  
Electron Microscope ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Scanning Transmission Electron Microscope ◽  
Acquisition Time ◽  
Thickness Variation ◽  
Transmission Electron ◽  
Distribution Of Elements ◽  
Scanning Transmission ◽  
Present Distribution

The recently developed parallel electron energy loss spectrometers (PEELS) have led to a significant reduction in spectrum acquisition time making EELS more useful in many applications in material science. Dwell times as short as 50 msec per spectrum with a PEELS coupled to a scanning transmission electron microscope (STEM), can make quantitative EEL images accessible. These images would present distribution of elements with the high spatial resolution inherent to EELS. The aim of this paper is to briefly investigate the effect of acquisition time per pixel on the signal to noise ratio (SNR), the effect of thickness variation and crystallography and finally the energy stability of spectra when acquired in the scanning mode during long periods of time.The configuration of the imaging system is the following: a Gatan PEELS is coupled to a CM30 (TEM/STEM) electron microscope, the control of the spectrometer and microscope is performed through a LINK AN10-85S MCA which is interfaced to a IBM RT 125 (running under AIX) via a DR11W line.

Low-cost high-performance CMOS terahertz imaging system

2015 ◽  
Author(s):  
Chung Hsing Li ◽  
Tzu-Chao Yan ◽  
Yuhsin Chang ◽  
Chyong Chen ◽  
Chien-Nan Kuo
Keyword(s):  
High Performance ◽  
Imaging System ◽  
Low Cost ◽  
Terahertz Imaging

Reflection type of terahertz imaging system using a high-Tc superconducting oscillator

2014 ◽  
Vol 104 (2) ◽  
pp. 022601 ◽  
Author(s):  
T. Kashiwagi ◽  
K. Nakade ◽  
B. Marković ◽  
Y. Saiwai ◽  
H. Minami ◽  
...  
Keyword(s):  
Imaging System ◽  
Terahertz Imaging ◽  
High Tc

Improvement of continuous-wave terahertz imaging system for cellulose acetate membrane electrophoresis

2012 ◽  
Vol 7 (S1) ◽  
pp. S126-S131 ◽  
Author(s):  
Hongbing Zhang ◽  
Kazutaka Mitobe ◽  
Mahmudul Kabir ◽  
Masafumi Suzuki ◽  
Yoko Mitobe ◽  
...  
Keyword(s):  
Cellulose Acetate ◽  
Continuous Wave ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Cellulose Acetate Membrane

Nano Sensor Using One Dimensional Porous Indium Oxide and Pattern Recognition Method of Its Electronic Information

2021 ◽  
Vol 16 (2) ◽  
pp. 255-263
Author(s):  
Qinghong Wu ◽  
Wanying Zhang
Keyword(s):  
Pattern Recognition ◽  
Indium Oxide ◽  
Electronic Nose ◽  
Sensor Array ◽  
Dimensional Space ◽  
Ordinary Least Squares ◽  
Acquisition Time ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Linear Discriminant

Due to its high sensitivity, low price and fast response speed, gas sensors based on metal oxide nanomate-rials have attracted many researchers to modify and explore the materials. First, pure indium oxide (In2O3) nanotubes (NTs)/porous NTs (PNTs) and Ho doped In2O3 NTs/PNTs are prepared by electrospinning and calcination. Then, based on the prepared nanomaterials, the 6-channel sensor array is obtained and used in the electronic nose sensing system for wine product identification. The system obtains the frequency signals of different liquor products by means of 6-channel sensor array, analyzes the extracted electronic signal characteristic information by means of ordinary least squares, and introduces the pattern recognition method of moving average and linear discriminant to identify liquor products. In the experiment, compared with pure In2O3 NTs sensor, pure In2O3 PNTs sensor has higher sensitivity to 100 ppm ethanol gas, and the sensitivity is further improved after mixing Ho. Among them, 6 mol% Ho + In2O3 PNTs have the highest sensitivity and the shortest response time; based on the electronic nose system composed of prepared nanomaterial sensor array, frequency signals of different Wu Liang Ye wines are collected. With the extension of acquisition time, the corresponding frequency first decreases and then becomes stable; the extracted liquor characteristic signal is projected into two-dimensional space and three-dimensional space. The results show that the pattern recognition system based on this method can extract the characteristic signals of liquor products and distinguish them.

Breast cancer margin detection with a single frequency terahertz imaging system

2016 ◽  
Author(s):  
Sigfrid K. Yngvesson ◽  
Andrew Karellas ◽  
Stephen Glick ◽  
Ashraf Khan ◽  
Paul R. Siqueira ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Single Frequency ◽  
Margin Detection

Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

2014 ◽  
Vol 989-994 ◽  
pp. 3946-3951
Author(s):  
Xin Jin ◽  
Ming Feng Jiang ◽  
Jie Feng
Keyword(s):  
Compressed Sensing ◽  
Group Structure ◽  
Direct Method ◽  
Group Sparsity ◽  
Mr Image ◽  
Assignment Method ◽  
Group Assignment ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Space Data

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

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