Wavefront Reconstruction Method for Aero-optical Distortion Based on Compressed Sensing

2021 ◽  
Author(s):  
Boyu Tian ◽  
Die Qiu ◽  
Ting He ◽  
zheqiang zhong ◽  
Bin Zhang
Keyword(s):  
Compressed Sensing ◽  
Optical Distortion ◽  
Reconstruction Method ◽  
Wavefront Reconstruction

Evaluation of a novel reconstruction method based on the compressed sensing technique: Application to cervical spine MR imaging

2019 ◽  
Vol 56 ◽  
pp. 140-145 ◽  
Author(s):  
Yuki Takato ◽  
Hirofumi Hata ◽  
Yusuke Inoue ◽  
Keiji Matsunaga ◽  
Toshimasa Hara ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Mr Imaging ◽  
Compressed Sensing ◽  
Reconstruction Method ◽  
Spine Mr

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. G83-G92
Author(s):  
Ya Xu ◽  
Fangzhou Nan ◽  
Weiping Cao ◽  
Song Huang ◽  
Tianyao Hao
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Gravity Data ◽  
Signal Reconstruction ◽  
Gaussian Function ◽  
Reconstruction Algorithm ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Grid Data ◽  
2D Data

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

scholarly journals A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5330
Author(s):  
Xiao ◽  
Hu ◽  
Shao ◽  
Li
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Low Complexity ◽  
Portable Devices ◽  
Reconstruction Method ◽  
Signal Recognition ◽  
Bioelectric Signal ◽  
Increasing Demand ◽  
Feature Values ◽  
Compression Sensing

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

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.

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