Gradient Projection for Sparse Reconstruction Method for Dynamic Fluorescence Molecular Tomography

2019 ◽  
pp. 581-592
Author(s):  
Jingxiao Fan ◽  
Hengna Zhao ◽  
Hongbo Guo ◽  
Yuqing Hou ◽  
Xiaowei He
Keyword(s):  
Gradient Projection ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Fluorescence Molecular Tomography ◽  
Dynamic Fluorescence

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.

Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System

2020 ◽  
Author(s):  
Jie Tian ◽  
Xiaopu Zhang ◽  
Yong Chen ◽  
Peter Russhard ◽  
Hua Ouyang
Keyword(s):  
Compressive Sensing ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Uniform Sampling ◽  
Blade Vibrations ◽  
Timing Data ◽  
Blade Tip ◽  
Simultaneous Identification ◽  
Multi Mode

Abstract Based on the blade vibration theory of turbomachinery and the basic principle of blade timing systems, a sparse reconstruction model is derived for the tip timing signal under an arbitrary sensor circumferential placement distribution. The proposed approach uses the sparsity of the tip timing signal in the frequency domain. The application of compressive sensing in reconstructing the blade tip timing signal and monitoring multi-mode blade vibrations is explored. To improve the reconstruction effect, a number of numerical experiments are conducted to examine the effects of various factors on synchronous and non-synchronous signals. This enables the specific steps involved in the compressive sensing reconstruction of tip timing signals to be determined. The proposed method is then applied to the tip timing data of a 27-blade rotor. The results show that the method accurately identifies the multi-mode blade vibrations at different rotation speeds. The proposed method has the advantages of low dependence on prior information, insensitivity to environmental noise, and simultaneous identification of synchronous and non-synchronous signals. The experimental results validate the effectiveness of the proposed approach in engineering applications.

scholarly journals POLARIMETRIC SAR TOMOGRAPHY USING l2,1 MIXED NORM SPARSE RECONSTRUCTION METHOD

2012 ◽  
Vol 130 ◽  
pp. 105-130 ◽  
Author(s):  
Shiqi Xing ◽  
Dahai Dai ◽  
Yongzhen Li ◽  
Xuesong Wang
Keyword(s):  
Sparse Reconstruction ◽  
Mixed Norm ◽  
Reconstruction Method ◽  
Polarimetric Sar

scholarly journals Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient

2018 ◽  
Vol 23 (08) ◽  
pp. 1
Author(s):  
Yuhao Liu ◽  
Shixin Jiang ◽  
Jie Liu ◽  
Yu An ◽  
Guanglei Zhang ◽  
...  
Keyword(s):  
Reconstruction Method ◽  
Fluorescence Molecular Tomography ◽  
L1 Norm

scholarly journals Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Riheng Wu ◽  
Yangyang Dong ◽  
Zhenhai Zhang ◽  
Le Xu
Keyword(s):  
Data Model ◽  
Doa Estimation ◽  
Nonsingular Matrix ◽  
Eigenvalue Decomposition ◽  
Estimation Methods ◽  
Sparse Reconstruction ◽  
Invariance Property ◽  
Reconstruction Method ◽  
Uniqueness Of Solutions ◽  
Moderate Complexity

We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.

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