Compressed sensing with cyclic-S Hadamard matrix for terahertz imaging applications

2018 ◽  
Author(s):  
Esra Şengün Ermeydan ◽  
İlyas Çankaya
Keyword(s):  
Compressed Sensing ◽  
Hadamard Matrix ◽  
Terahertz Imaging

Terahertz imaging with compressed sensing and phase retrieval

2008 ◽  
Vol 33 (9) ◽  
pp. 974 ◽  
Author(s):  
Wai Lam Chan ◽  
Matthew L. Moravec ◽  
Richard G. Baraniuk ◽  
Daniel M. Mittleman
Keyword(s):  
Compressed Sensing ◽  
Phase Retrieval ◽  
Terahertz Imaging

Approaching real‐time terahertz imaging with photo‐induced coded apertures and compressed sensing

2014 ◽  
Vol 50 (11) ◽  
pp. 801-803 ◽  
Author(s):  
M.I.B. Shams ◽  
Z. Jiang ◽  
S. Rahman ◽  
J. Qayyum ◽  
L.‐J. Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Real Time ◽  
Terahertz Imaging ◽  
Coded Apertures

scholarly journals Double-Image Visually Meaningful Encryption Algorithm Based on Compressed Sensing and Frft Embedding

2021 ◽  
Author(s):  
donghua jiang ◽  
Lidong Liu ◽  
Liya Zhu ◽  
Xingyuan Wang ◽  
Yingpin Chen ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Fractional Fourier Transform ◽  
Hadamard Matrix ◽  
Cellular Neural Network ◽  
The Internet ◽  
Double Image ◽  
Security Issues ◽  
Plain Image ◽  
Encrypted Images ◽  
Privacy Issues

Abstract The transmission of images via the Internet has grown exponentially in the past few decades. However, the Internet considered as an insecure method of information transmission may cause serious privacy issues. In order to overcome such potential security issues, a novel double-image visually meaningful encryption (DIVME) algorithm conjugating quantum cellular neural network (QCNN), compressed sensing (CS) and fractional Fourier transform (FRFT) is proposed in this paper. First, the wavelet coefficients of the two plain images are scrambled by the Fisher-Yates confusion algorithm, and then compressed by the key-controlled partial Hadamard matrix. The final meaningful cipher image is generated by embedding the encrypted images into a host image with the same resolution of the plain image via the FRFT-based embedding method. Besides, the eigenvalues of the plain images are utilized to generate the key stream to improve the ability of proposed DIVME algorithm to withstand the plaintext attacks. Afterwards, the plaintext eigenvalues are embedded into the alpha channel of the meaningful cipher image under control of the keys to reduce unnecessary storage space and transmission costs. Ultimately, the simulation results and security analyses indicate that the proposed DIVME algorithm is effective and can withstand multiple attacks.

A Terahertz Compressive Imaging Method Based on Single Detector of Randomly Moving Template

2013 ◽  
Vol 756-759 ◽  
pp. 3785-3788
Author(s):  
Sai Qi Shang ◽  
Min Gang Wang ◽  
Wei Li ◽  
Yao Yang
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Matching Pursuit ◽  
Terahertz Imaging ◽  
Compressive Sampling ◽  
Imaging Method ◽  
Original Image ◽  
Reconstruction Algorithms ◽  
Compressive Imaging ◽  
Single Detector

Expensiveness and lack of N-pixels sensor affect the application of terahertz imaging. New compressed sensing theory recently achieved a major breakthrough in the field of signal codec, making it possible to recover the original image by using the measured values, which have much smaller number than the pixels in the image. In this paper, by comparing the measurement matrices based on different reconstruction algorithms, such as Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit and Minimum L_1 Norm algorithms, we proposed a terahertz imaging method based on single detector of randomly moving measurement matrices, designed the mobile random templates and an automatically template changing mechanism, constructed a single detector imaging system, and completed the single terahertz detector imaging experiments.

Single-pixel terahertz imaging via compressed sensing

2011 ◽  
Author(s):  
Ya-qin Zhao ◽  
Liang-liang Zhang ◽  
Guo-teng Duan ◽  
Xiao-hua Liu ◽  
Cun-lin Zhang
Keyword(s):  
Compressed Sensing ◽  
Terahertz Imaging ◽  
Single Pixel

A Fast Spatial-domain Terahertz Imaging Using Block-based Compressed Sensing

2011 ◽  
Vol 32 (11) ◽  
pp. 1328-1336 ◽  
Author(s):  
Byung-Min Hwang ◽  
Sang Hun Lee ◽  
Woo-Taek Lim ◽  
Chang-Beom Ahn ◽  
Joo-Hiuk Son ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Terahertz Imaging ◽  
Spatial Domain ◽  
Block Based

A single-pixel terahertz imaging system based on compressed sensing

2008 ◽  
Vol 93 (12) ◽  
pp. 121105 ◽  
Author(s):  
Wai Lam Chan ◽  
Kriti Charan ◽  
Dharmpal Takhar ◽  
Kevin F. Kelly ◽  
Richard G. Baraniuk ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Single Pixel
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