scholarly journals Group Sparse Basis Pursuit Denoising Reconstruction Algorithm for Polarimetric Through-the-Wall Radar Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lele Qu ◽  
Shimiao An ◽  
Tianhong Yang ◽  
Yanpeng Sun
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Projection ◽  
Radar Imaging ◽  
Basis Pursuit ◽  
Imaging Method ◽  
L1 Norm ◽  
Reconstruction Performance ◽  
Imaging Reconstruction ◽  
Imaging Algorithms

Polarimetric through-the-wall radar imaging (TWRI) system has the enhancing performance in the detection, imaging, and classification of concealed targets behind the wall. We propose a group sparse basis pursuit denoising- (BPDN-) based imaging approach for polarimetric TWRI system in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with the nonuniform fast Fourier transform (NUFFT) technique to implement the imaging reconstruction of observed scene. The experimental results have demonstrated that compared to the existing compressive sensing- (CS-) based imaging algorithms, the proposed NUFFT-based SPGL1 algorithm can significantly reduce the required computer memory and achieve the improved imaging reconstruction performance with the high computational efficiency.

scholarly journals An Industrial B-Mode Phased Array Ultrasonic Imaging Reconstruction Algorithm Based on FRI Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Song Lin
Keyword(s):  
Phased Array ◽  
Simulation Experiment ◽  
Ultrasonic Imaging ◽  
Reconstruction Algorithm ◽  
Imaging Method ◽  
Sampling Point ◽  
Simple Circuit ◽  
Ultrasonic Signals ◽  
Imaging Algorithm ◽  
Imaging Reconstruction

Firstly, a novel FRI sampling model has been proposed according to the characteristics of ultrasonic signals. The model has the advantages such as good stability, strong antinoise ability, simple circuit implementation, and fewer preconditions, compared to the traditional methods. Then, in order to verify the validity of the sampling model, the method is applied to B-type ultrasonic imaging, and a B-type phased array ultrasonic imaging algorithm based on FRI sampling model is proposed. Finally, the algorithm simulation experiment is designed, and the results show that the sampling point required by the proposed FRI sampling model is only 0.1% of the traditional B-type phased array ultrasonic imaging method, and the sampling frequency of the proposed ultrasonic imaging algorithm is only 0.0077% of the traditional B-type ultrasonic imaging method. Additionally, the experiment result indicates that this algorithm is more applicable to phased array ultrasonic imaging than the SOS filter is.

Group sparsity based imaging algorithm for TWRI under wall parameter uncertainties

2016 ◽  
Vol 40 (1) ◽  
pp. 251-260 ◽  
Author(s):  
Yanpeng Sun ◽  
Shi Zhang ◽  
Zhao Cui
Keyword(s):  
High Resolution ◽  
Matching Pursuit ◽  
Multipath Propagation ◽  
Reconstruction Algorithm ◽  
Imaging Method ◽  
Group Sparsity ◽  
Parameter Uncertainties ◽  
Reconstruction Procedure ◽  
Imaging Algorithm ◽  
Imaging Algorithms

Through-the-wall radar imaging (TWRI) applications allow accurate target localization and high-resolution imaging. However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.

scholarly journals Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging

2021 ◽  
Vol 13 (4) ◽  
pp. 600
Author(s):  
Jixiang Fu ◽  
Mengdao Xing ◽  
Guangcai Sun
Keyword(s):  
Spectrum Analysis ◽  
Near Field ◽  
Radar Imaging ◽  
Radar Signal ◽  
Imaging Method ◽  
Imaging Geometry ◽  
Time Frequency ◽  
Isar Imaging ◽  
Low Efficiency ◽  
Imaging Algorithms

Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.

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.

An lp-based reconstruction algorithm for compressed sensing radar imaging

2016 ◽  
Author(s):  
Le Zheng ◽  
Arian Maleki ◽  
Quanhua Liu ◽  
Xiaodong Wang ◽  
Xiaopeng Yang
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Radar Imaging

scholarly journals Classification of Pneumonia Cell Images Using Improved ResNet50 Model

2021 ◽  
Vol 38 (1) ◽  
pp. 165-173
Author(s):  
Ahmet Çınar ◽  
Muhammed Yıldırım ◽  
Yeşim Eroğlu
Keyword(s):  
Error Rates ◽  
Lung Imaging ◽  
Imaging Method ◽  
Data Sets ◽  
Accuracy Rate ◽  
Traditional Methods ◽  
X Ray ◽  
Qualified Personnel ◽  
Patient Will

Pneumonia is a disease caused by inflammation of the lung tissue that is transmitted by various means, primarily bacteria. Early and accurate diagnosis is important in reducing the morbidity and mortality of the disease. The primary imaging method used for the diagnosis of pneumonia is lung x-ray. While typical imaging findings of pneumonia may be present on lung imaging, nonspecific images may be present. In addition, many health units may not have qualified personnel to perform this procedure or there may be errors in diagnoses made by traditional methods. For this reason, computer systems can be used to prevent error rates that may occur in traditional methods. Many methods have been developed to train data sets. In this article, a new model has been developed based on the layers of the ResNet50. The developed model was compared with the architectures InceptionV3, AlexNet, GoogleNet, ResNet50 and DenseNet201. In the developed model, the maximum accuracy rate was achieved as 97.22%. The model developed was followed by DenseNet201, ResNet50, InceptionV3, GoogleNet and AlexNet, respectively, according to their accuracy. With these developed models, the diagnosis of pneumonia can be made early and accurately, and the treatment management of the patient will be determined quickly.

scholarly journals A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 126 ◽  
Author(s):  
Bin Wang ◽  
Li Wang ◽  
Hao Yu ◽  
Fengming Xin
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Steepest Descent Method ◽  
Signal Recovery ◽  
Two Dimensional ◽  
Reconstruction Algorithms ◽  
Experimental Conditions ◽  
One Dimensional ◽  
Recovery Algorithm ◽  
Reconstruction Performance

The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0) algorithm, achieving the reconstruction of the signal well in the presence of noise. However, the ReSL0 reconstruction algorithm still has some flaws. It still chooses the original optimization method of SL0 and the Gauss approximation function, but this method has the problem of a sawtooth effect in the later optimization stage, and the convergence effect is not ideal. Therefore, we make two adjustments to the basis of the ReSL0 reconstruction algorithm: firstly, we introduce another CIPF function which has a better approximation effect than Gauss function; secondly, we combine the steepest descent method and Newton method in terms of the algorithm optimization. Then, a novel regularized recovery algorithm named combined regularized smooth L0 (CReSL0) is proposed. Under the same experimental conditions, the CReSL0 algorithm is compared with other popular reconstruction algorithms. Overall, the CReSL0 algorithm achieves excellent reconstruction performance in terms of the peak signal-to-noise ratio (PSNR) and run-time for both a one-dimensional Gauss signal and two-dimensional image reconstruction tasks.

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