Optimized Measurement Matrix for Compressive Sensing

2013 ◽  
Vol 12 (1) ◽  
pp. 71-86
Author(s):  
Honglin Huang ◽  
Anamitra Makur
Keyword(s):  
Compressive Sensing ◽  
Measurement Matrix

scholarly journals Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

2019 ◽  
Vol 9 (21) ◽  
pp. 4596 ◽  
Author(s):  
Tongjing Sun ◽  
Ji Li ◽  
Philippe Blondel
Keyword(s):  
Compressive Sensing ◽  
A Priori ◽  
Original Data ◽  
Measurement Matrix ◽  
Critical Elements ◽  
Under Sampling ◽  
Information Sampling ◽  
Priori Information ◽  
Practical Field ◽  
Dedicated Hardware

Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

Measurement matrix construction by specklegrams from step index multimode fiber and its application in compressive sensing

Optik ◽  
2020 ◽  
Vol 220 ◽  
pp. 164783
Author(s):  
Qi Qin ◽  
Yan Liu ◽  
Zhongwei Tan ◽  
Muguang Wang ◽  
Fengping Yan
Keyword(s):  
Compressive Sensing ◽  
Multimode Fiber ◽  
Measurement Matrix ◽  
Step Index ◽  
Matrix Construction

scholarly journals Image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix

2018 ◽  
Vol 13 ◽  
pp. 174830181879151
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Compressive Sensing ◽  
Color Image ◽  
Singular Value ◽  
Measurement Matrix ◽  
Fusion Algorithm ◽  
Full Color ◽  
Value Decomposition ◽  
Time And Space Complexity

To solve the problem of high time and space complexity of traditional image fusion algorithms, this paper elaborates the framework of image fusion algorithm based on compressive sensing theory. A new image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix is proposed. This proposed algorithm only acts on a small amount of measurement data after compressive sensing sampling, which greatly reduces the number of pixels involved in the fusion and improves the time and space complexity of fusion. In the fusion experiments of full-color image with multispectral image, infrared image with visible light image, as well as multispectral image with full-color image, this proposed algorithm achieved good experimental results in the evaluation parameters of information entropy, standard deviation, average gradient, and mutual information.

Compressive sensing measurement matrix construction based on improved size compatible array LDPC code

2015 ◽  
Vol 9 (11) ◽  
pp. 993-1001 ◽  
Author(s):  
Haiying Yuan ◽  
Hongying Song ◽  
Xun Sun ◽  
Kun Guo ◽  
Zijian Ju
Keyword(s):  
Compressive Sensing ◽  
Ldpc Code ◽  
Measurement Matrix ◽  
Matrix Construction

Spatial filter measurement matrix design for interference/jamming suppression in colocated compressive sensing MIMO radars

2016 ◽  
Vol 52 (11) ◽  
pp. 956-958 ◽  
Author(s):  
Yu Tao ◽  
Gong Zhang ◽  
Yu Zhang
Keyword(s):  
Compressive Sensing ◽  
Spatial Filter ◽  
Measurement Matrix ◽  
Matrix Design

scholarly journals A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5327-5342 ◽  
Author(s):  
Sanjeev Sharma ◽  
Anubha Gupta ◽  
Vimal Bhatia
Keyword(s):  
Compressive Sensing ◽  
Sparse Signal ◽  
Measurement Matrix ◽  
Uwb Communication

Analysis of the impact of measurement matrices in compressive sensing for medical images

2020 ◽  
pp. 591-600
Author(s):  
G. Kowsalya ◽  
H. A. Christinal ◽  
D. A. Chandy ◽  
S. Jebasingh ◽  
C. Bajaj
Keyword(s):  
Compressive Sensing ◽  
Sparse Representation ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Basis Pursuit ◽  
Measurement Matrix ◽  
Compressed Images ◽  
The Impact ◽  
Reconstruction Time

Compressive sensing of images is based on three key components namely sparse representation, construction of measurement matrix and reconstruction of images. The visual quality of reconstructed image is prime important in medical images. We apply Discrete Cosine Transform (DCT) for sparse representation of medical images. This paper focuses on the analysis of measurement matrices on compressive sensing of MRI images. In this work, the Gaussian and Bernoulli type of random matrices are considered as measurement matrix. The compressed images are reconstructed using Basis Pursuit algorithm. Peak-signal-to noise ratio and reconstruction time are the metrics taken for evaluating the performance of measurement matrices towards compressive sensing of medical images.

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