A compressed sensing imaging method based on radar signal construct sparsity basis

2013 ◽  
Author(s):  
Sun Wen-feng ◽  
Fan Xiao-yan ◽  
Song Da-wei ◽  
Li Dong ◽  
Han Tao ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Radar Signal ◽  
Imaging Method

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.

scholarly journals Photoacoustic imaging method based on arc-direction compressed sensing and multi-angle observation

2011 ◽  
Vol 19 (16) ◽  
pp. 14801 ◽  
Author(s):  
Mingjian Sun ◽  
Naizhang Feng ◽  
Yi Shen ◽  
Xiangli Shen ◽  
Liyong Ma ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Photoacoustic Imaging ◽  
Imaging Method ◽  
Angle Observation

Imaging of Transmission Equipment Based on Block Compressed Sensing

2012 ◽  
Vol 190-191 ◽  
pp. 998-1001 ◽  
Author(s):  
Jing Jing Zhao ◽  
Ji Xiang Sun ◽  
Shi Lin Zhou ◽  
Lei Hu
Keyword(s):  
Compressed Sensing ◽  
Image Data ◽  
Inspection System ◽  
Imaging Method ◽  
Fault Diagnose ◽  
High Quality ◽  
Transmission Equipment ◽  
Block Compressed Sensing ◽  
Simulation Results ◽  
Memory Resources

Imaging the overhead transmission equipment with high-resolution is very important to intelligent inspection, which is the prerequisites for fault diagnose. The intelligent inspection system often takes traditional imaging process of data acquisition followed by compression, which leads to the waste of image data and memory resources. We adopt an imaging method based on block compressed sensing to image the transmission equipment, the simulation results show that even if we only compressively sampled with 12.5% of the fully acquired image data, the image still can be recovered with high quality.

Radar Compressed Sensing Imaging Method with Two-Dimensional Separable Sampling

2014 ◽  
Vol 989-994 ◽  
pp. 3755-3758
Author(s):  
Shu Hong Jiao ◽  
Lin Tang ◽  
Xue Liu ◽  
Huan Qi
Keyword(s):  
Compressed Sensing ◽  
Performance Improvement ◽  
Radar Imaging ◽  
Two Dimensions ◽  
New Method ◽  
Imaging Method ◽  
Two Dimensional ◽  
Memory Usage ◽  
Visible Imaging ◽  
Imaging Performance

A radar compressed sensing imaging method with 2-D separable sampling is proposed in this paper. Instead of converting the radar imaging problem into two 1-D compressed sensing problem, we use the 2-D Separable Projections to solve it directly. Unlike the 2-D separable sampling in visible imaging, the range and azimuth which are the two dimensions of the radar imaging couple with each other. This Coupling increases the storage and computation in radar compressed imaging, therefore some de-coupling processing using in Range Doppler algorithm are adopted in the proposed method to construct the 2-D separable sampling data. Accordingly the two dimensional scene has been reconstructed with the proposed 2-D compressed sensing algorithms. Compared with conventional compressed sensing imaging methods, the new method has reduced the memory usage and complexity with imaging performance improvement.

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