scholarly journals Compressed Sensing Radar Imaging With Magnitude Sparse Representation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29722-29733 ◽  
Author(s):  
Jungang Yang ◽  
Tian Jin ◽  
Xiaotao Huang
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Radar Imaging

Mixed sparse representation for approximated observation-based compressed sensing radar imaging

2018 ◽  
Vol 12 (03) ◽  
pp. 1 ◽  
Author(s):  
Bo Li ◽  
Falin Liu ◽  
Chongbin Zhou ◽  
Zheng Wang ◽  
Hao Han
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Radar Imaging

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

An lp-based reconstruction algorithm for compressed sensing radar imaging

2016 ◽  
Author(s):  
Le Zheng ◽  
Arian Maleki ◽  
Quanhua Liu ◽  
Xiaodong Wang ◽  
Xiaopeng Yang
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Radar Imaging

Study on Over-Complete Dictionaries for Sparse Representations of Signals

2012 ◽  
Vol 157-158 ◽  
pp. 796-799
Author(s):  
Guang Chun Gao ◽  
Kai Xiong ◽  
Li Na Shang ◽  
Sheng Ying Zhao ◽  
Cui Zhang
Keyword(s):  
Inverse Problems ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Sparse Representations ◽  
Experimental Results ◽  
Decomposition Algorithm ◽  
Linear Combinations ◽  
Design Algorithm ◽  
Sparse Decomposition

In recent years there has been a growing interest in the study of sparse representation of signals. The redundancy of over-complete dictionary can make it effectively capture the characteristics of the signals. Using an over-complete dictionary that contains prototype signal-atoms, signals are described as linear combinations of a few of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, Compressed Sensing (CS), and more. Recent activities in this field concentrate mainly on the study of sparse decomposition algorithm and dictionary design algorithm. In this paper, we discuss the advantages of sparse dictionaries, and present the implicit dictionaries for signal sparse presents. The overcomplete dictionaries which combined the different orthonormal transform bases can be used for the compressed sensing. Experimental results demonstrate the effectivity for sparse presents of signals.

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