Compressive sensing with variable density sampling for 3D imaging

2019 ◽  
Author(s):  
Adrian Stern ◽  
Vladislav Kravets ◽  
Yair Rivenson ◽  
Bahram Javidi
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Variable Density

scholarly journals Compressive Sensing Based Three-Dimensional Imaging Method with Electro-Optic Modulation for Nonscanning Laser Radar

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 748
Author(s):  
Yulong An ◽  
Yanmei Zhang ◽  
Haichao Guo ◽  
Jing Wang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Low Cost ◽  
Functional Model ◽  
Three Dimensional ◽  
Visible Spectrum ◽  
Depth Map ◽  
Imaging Method ◽  
Practical Applications ◽  
Electro Optic

Low-cost Laser Detection and Ranging (LiDAR) is crucial to three-dimensional (3D) imaging in applications such as remote sensing, target detection, and machine vision. In conventional nonscanning time-of-flight (TOF) LiDAR, the intensity map is obtained by a detector array and the depth map is measured in the time domain which requires costly sensors and short laser pulses. To overcome such limitations, this paper presents a nonscanning 3D laser imaging method that combines compressive sensing (CS) techniques and electro-optic modulation. In this novel scheme, electro-optic modulation is applied to map the range information into the intensity of echo pulses symmetrically and the measurements of pattern projection with symmetrical structure are received by the low bandwidth detector. The 3D imaging can be extracted from two gain modulated images that are recovered by solving underdetermined inverse problems. An integrated regularization model is proposed for the recovery problems and the minimization functional model is solved by a proposed algorithm applying the alternating direction method of multiplier (ADMM) technique. The simulation results on various subrates for 3D imaging indicate that our proposed method is feasible and achieves performance improvement over conventional methods in systems with hardware limitations. This novel method will be highly valuable for practical applications with advantages of low cost and flexible structure at wavelengths beyond visible spectrum.

Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI

2015 ◽  
Vol 76 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Giuseppe Valvano ◽  
Nicola Martini ◽  
Luigi Landini ◽  
Maria Filomena Santarelli
Keyword(s):  
Compressive Sensing ◽  
Variable Density

scholarly journals 3D Imaging Algorithm for Down-Looking MIMO Array SAR Based on Bayesian Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaozhen Ren ◽  
Lina Chen ◽  
Jing Yang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Additive Noise ◽  
Signal Reconstruction ◽  
3D Images ◽  
High Quality Image ◽  
Quality Image ◽  
New Strategy ◽  
Noisy Measurements ◽  
Cross Track

Down-looking MIMO array SAR can reconstruct 3D images of the observed area in the inferior of the platform of the SAR and has wide application prospects. In this paper, a new strategy based on Bayesian compressive sensing theory is proposed for down-looking MIMO array SAR imaging, which transforms the cross-track imaging process of down-looking MIMO array SAR into the problem of sparse signal reconstruction from noisy measurements. Due to account for additive noise encountered in the measurement process, high quality image can be achieved. Simulation results indicate that the proposed method can provide better resolution and lower sidelobes compared to the conventional method.

3D Imaging with Compressive Sensing

2013 ◽  
Author(s):  
Abhijit Mahalanobis ◽  
Xiao Xiao ◽  
Yair Rivenson ◽  
Ryoichi Horisaki ◽  
Adrian Stern ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
3D Imaging

Photon-counting compressive sensing laser radar for 3D imaging

2011 ◽  
Vol 50 (31) ◽  
pp. 5917 ◽  
Author(s):  
G. A. Howland ◽  
P. B. Dixon ◽  
J. C. Howell
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Photon Counting ◽  
Laser Radar
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