Reconstruction Algorithm of Ultrasonic CT Based on Matching Pursuit

2014 ◽  
Author(s):  
Lingjun Liu ◽  
Zhonghua Xie ◽  
Cui Yang
Keyword(s):  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Ultrasonic Ct

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

scholarly journals A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

2014 ◽  
Vol 7 (5) ◽  
pp. 1901-1918 ◽  
Author(s):  
J. Ray ◽  
V. Yadav ◽  
A. M. Michalak ◽  
B. van Bloemen Waanders ◽  
S. A. McKenna
Keyword(s):  
Spatial Variability ◽  
Carbon Dioxide Emissions ◽  
Fossil Fuel ◽  
Matching Pursuit ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Spatiotemporal Variability ◽  
Realistic Representation ◽  
Non Gaussian ◽  
Low Dimensional

Abstract. The characterization of fossil-fuel CO2 (ffCO2) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based) spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a~parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

scholarly journals A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction

2018 ◽  
Vol 173 ◽  
pp. 03073
Author(s):  
Liu Yang ◽  
Ren Qinghua ◽  
Xu Bingzheng ◽  
Li Xiazhao
Keyword(s):  
Time Use ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Transform Domain ◽  
Low Snr ◽  
System Noise ◽  
Broadband Spectrum ◽  
Matching Pursuit Algorithm ◽  
The Mean ◽  
Occupancy Rates

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

scholarly journals Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018 ◽  
Vol 11 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Jingjing Yu ◽  
Qiyue Li ◽  
Haiyu Wang
Keyword(s):  
Image Quality ◽  
Matching Pursuit ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Preclinical Research ◽  
Bioluminescence Tomography ◽  
Comparison Results ◽  
Stable Reconstruction

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

A novel topology identification method based on compressive sensing for multidimensional networks

2020 ◽  
Vol 34 (30) ◽  
pp. 2050294
Author(s):  
Shuheng Fang ◽  
Zhengmin Kong ◽  
Ping Hu ◽  
Li Ding
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Network Models ◽  
Reconstruction Algorithm ◽  
Identification Problem ◽  
Topology Identification ◽  
Network Recovery ◽  
Threshold Mechanism ◽  
Different Types ◽  
2D Network

In real-world scenarios, it is difficult to know about the complete topology of a huge network with different types of links. In this brief, we propose a method to identify the topology of multidimensional networks from information transmission data. We consider information propagating over edges of a two-dimensional (2D) network, where one type of links is known and the other type is unknown. Given the state of all nodes at each unit time, we can transform the topology identification problem into a compressive sensing framework. A modified reconstruction algorithm, called Sparsity Adaptive Matching Pursuit with Mixed Threshold Mechanism (SAMPMTM), is proposed to tackle the problem. Compared with the classical Sparsity Adaptive Matching Pursuit (SAMP) algorithm, the proposed SAMPMTM algorithm can reduce the conflict rate and improve the accuracy of network recovery. We further demonstrate the performance of this improved algorithm through Monte-Carlo simulations under different network models.

scholarly journals Image reconstruction algorithm based on variable atomic number matching pursuit

2016 ◽  
Vol 11 (2) ◽  
pp. 103-109
Author(s):  
Hongtu Zhao ◽  
Chong Chen ◽  
Chenxu Shi
Keyword(s):  
Image Reconstruction ◽  
Convex Optimization ◽  
Atomic Number ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Optimization Algorithms ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Reconstruction Quality ◽  
Stable Reconstruction

As the most critical part of compressive sensing theory, reconstruction algorithm has an impact on the quality and speed of image reconstruction. After studying some existing convex optimization algorithms and greedy algorithms, we find that convex optimization algorithms should possess higher complexity to achieve higher reconstruction quality. Also, fixed atomic numbers used in most greedy algorithms increase the complexity of reconstruction. In this context, we propose a novel algorithm, called variable atomic number matching pursuit, which can improve the accuracy and speed of reconstruction. Simulation results show that variable atomic number matching pursuit is a fast and stable reconstruction algorithm and better than the other reconstruction algorithms under the same conditions.

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