Fast high-quality sparse reconstruction of photoacoustic imaging based on HTP compressed sensing

2021 ◽  
Author(s):  
Jiaqi Tang ◽  
Aojie Zhao ◽  
Bo Li ◽  
Xianlin Song
Keyword(s):  
Compressed Sensing ◽  
Photoacoustic Imaging ◽  
Sparse Reconstruction ◽  
High Quality

scholarly journals Compressed Sensing-Based Range-Doppler Processing Method for Passive Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xia Bai ◽  
Hejing Guo ◽  
Juan Zhao ◽  
Tao Shan
Keyword(s):  
Compressed Sensing ◽  
Cross Correlation ◽  
Processing Method ◽  
Sparse Reconstruction ◽  
Digital Television ◽  
Experimental Measurements ◽  
Passive Radar ◽  
Computational Burden ◽  
Chinese Standard ◽  
Classical Cross

Passive radar (PR) systems use the existing transmitters of opportunity in the environment to perform tasks such as detection, tracking, and imaging. The classical cross-correlation based methods to obtain the range-Doppler map have the problems of high sidelobe and limited resolution due to the influence of signal bandwidth. In this paper, we propose a novel range-Doppler processing method based on compressed sensing (CS), which performs sparse reconstruction in range and Doppler dimensions to achieve high resolution and reduces sidelobe without excessive computational burden. Results from numerical simulations and experimental measurements recorded with the Chinese standard digital television terrestrial broadcasting (DTTB) based PR show that the proposed method successfully handles the range-Doppler map formatting problem for PR and outperforms the existing CS-based PR processing methods.

scholarly journals Sparse reconstruction with multiple Walsh matrices

2019 ◽  
Vol 10 (3) ◽  
pp. 221-239
Author(s):  
Enrico Au-Yeung
Keyword(s):  
Compressed Sensing ◽  
Dimensionality Reduction ◽  
Vector Space ◽  
Sparse Reconstruction ◽  
Dimensional Vector ◽  
Dimensional Vector Space ◽  
Computational Harmonic Analysis ◽  
Sparse Vector ◽  
Class Of Matrices ◽  
Lower Dimensional

Abstract The problem of how to find a sparse representation of a signal is an important one in applied and computational harmonic analysis. It is closely related to the problem of how to reconstruct a sparse vector from its projection in a much lower-dimensional vector space. This is the setting of compressed sensing, where the projection is given by a matrix with many more columns than rows. We introduce a class of random matrices that can be used to reconstruct sparse vectors in this paradigm. These matrices satisfy the restricted isometry property with overwhelming probability. We also discuss an application in dimensionality reduction where we initially discovered this class of matrices.

Medical Image Compressed Sensing Reconstruction

2014 ◽  
Vol 556-562 ◽  
pp. 4835-4838 ◽  
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu ◽  
Yu Ming Sun
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Medical Image ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Sparse Reconstruction ◽  
Image Reconstruction Algorithm ◽  
Gradient Direction ◽  
Medical Image Reconstruction ◽  
Negative Gradient

In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

scholarly journals Real-time 3D Photoacoustic Visualization System with a Wide Field of View for Imaging Human Limbs

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1813 ◽  
Author(s):  
Kenichi Nagae ◽  
Yasufumi Asao ◽  
Yoshiaki Sudo ◽  
Naoyuki Murayama ◽  
Yuusuke Tanaka ◽  
...  
Keyword(s):  
Photoacoustic Imaging ◽  
Motion Pictures ◽  
Field Of View ◽  
Prototype System ◽  
Wide Field ◽  
High Quality ◽  
Still Images ◽  
Wide Range ◽  
The Subject ◽  
System Prototype

Background: A breast-specific photoacoustic imaging (PAI) system prototype equipped with a hemispherical detector array (HDA) has been reported as a promising system configuration for providing high morphological reproducibility for vascular structures in living bodies. Methods: To image the vasculature of human limbs, a newly designed PAI system prototype (PAI-05) with an HDA with a higher density sensor arrangement was developed. The basic device configuration mimicked that of a previously reported breast-specific PAI system. A new imaging table and a holding tray for imaging a subject's limb were adopted. Results: The device’s performance was verified using a phantom. Contrast of 8.5 was obtained at a depth of 2 cm, and the viewing angle reached up to 70 degrees, showing sufficient performance for limb imaging. An arbitrary wavelength was set, and a reasonable PA signal intensity dependent on the wavelength was obtained. To prove the concept of imaging human limbs, various parts of the subject were scanned. High-quality still images of a living human with a wider size than that previously reported were obtained by scanning within the horizontal plane and averaging the images. The maximum field of view (FOV) was 270 mm × 180 mm. Even in movie mode, one-shot 3D volumetric data were obtained in an FOV range of 20 mm in diameter, which is larger than values in previous reports. By continuously acquiring these images, we were able to produce motion pictures. Conclusion: We developed a PAI prototype system equipped with an HDA suitable for imaging limbs. As a result, the subject could be scanned over a wide range while in a more comfortable position, and high-quality still images and motion pictures could be obtained.

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