scholarly journals A Photoacoustic Imaging Algorithm Based on Regularized Smoothed L0 Norm Minimization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xueyan Liu ◽  
Limei Zhang ◽  
Yining Zhang ◽  
Lishan Qiao
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Imaging ◽  
Sparse Sampling ◽  
Signal Recovery ◽  
L1 Norm ◽  
High Quality ◽  
Norm Minimization ◽  
Reconstruction Quality ◽  
Noisy Measurement ◽  
Better Than

The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant regularized smooth L0 (ReSL0) algorithm for PAI image reconstruction, which has the same computational advantages as the SL0 algorithm while having a higher degree of immunity to inaccuracy caused by noise. In order to evaluate the performance of the ReSL0 algorithm, we reconstruct the simulated dataset obtained from three phantoms. In addition, a real experimental dataset from agar phantom is also used to verify the effectiveness of the ReSL0 algorithm. Compared to three L0 norm, L1 norm, and TV norm-based CS algorithms for signal recovery and image reconstruction, experiments demonstrated that the ReSL0 algorithm provides a good balance between the quality and efficiency of reconstructions. Furthermore, the PSNR of the reconstructed image calculated by the introduced method was better than the other three methods. In particular, it can notably improve reconstruction quality in the case of noisy measurement.

Efficient L1/2 Regularization-Based Reconstruction for Photoacoustic Imaging Using Adaptively Iterative Thresholding Algorithm

2020 ◽  
Vol 10 (7) ◽  
pp. 1506-1514
Author(s):  
Xueyan Liu ◽  
Xu Kong ◽  
Lishan Qiao ◽  
Jianli Zhao
Keyword(s):  
Simulation Analysis ◽  
Photoacoustic Imaging ◽  
Sparse Sampling ◽  
Signal Recovery ◽  
L1 Norm ◽  
Preclinical Research ◽  
Iterative Thresholding ◽  
Reconstruction Methods ◽  
Significant Research ◽  
Thresholding Algorithm

Sparse sampling photoacoustic imaging (PAI) is a significant research topic. Accurate and efficient reconstruction methods play important roles in the wide application of PAI in preclinical research. Compressed sensing has large potential in efficiently reconstructing high quality PAI images with sparse sampling signal. We presented our study on a L1/2 regularization operator based adaptive iterative thresholding method for sparse view PAI in this article, which can use few measurements to exactly reconstruct the PAI image. The effectiveness of the proposed algorithm was verified by simulation analysis using Matlab. And the proposed algorithm was compared with two state-of-the-art L1 norm and TV norm based algorithms for signal recovery and image reconstruction. The SNR of the reconstructed image calculated by the introduced method was better than the other two methods tested in this research. The algorithm presented in this paper not only provides higher quality image with sparse view data, but also is robust to noise and over estimation of sparsity value.

scholarly journals Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction

2015 ◽  
Vol 15 (7) ◽  
pp. 77-87
Author(s):  
A. Lazarov ◽  
D. Minchev
Keyword(s):  
Image Reconstruction ◽  
Sar Image ◽  
Two Dimensional ◽  
Reconstruction Problem ◽  
L1 Norm ◽  
One Dimensional ◽  
Sparse Decomposition ◽  
Norm Minimization ◽  
The Earth ◽  
Definition Of

Abstract A nonconventional image algorithm, based on compressed sensing and l1-norm minimization in Synthetic Aperture Radar (SAR) application is discussed. A discrete model of the earth surface relief and mathematical modeling of SAR signal formation are analytically described. Sparse decomposition in Fourier basis to solve the SAR image reconstruction problem is discussed. In contrast to the classical one-dimensional definition of l1-norm minimization in SAR image reconstruction, applied to an image vector, the present work proposes a two-dimensional definition of l1-norm minimization to the image. To verify the correctness of the algorithm, results of numerical experiments are presented.

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging

2020 ◽  
Vol 6 (4) ◽  
pp. 045019 ◽  
Author(s):  
Parastoo Farnia ◽  
Mohammad Mohammadi ◽  
Ebrahim Najafzadeh ◽  
Maysam Alimohamadi ◽  
Bahador Makkiabadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Photoacoustic Imaging ◽  
Deep Convolutional Neural Network ◽  
High Quality

scholarly journals Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

2021 ◽  
Vol 13 (14) ◽  
pp. 2812
Author(s):  
Changyu Hu ◽  
Ling Wang ◽  
Daiyin Zhu ◽  
Otmar Loffeld
Keyword(s):  
Image Reconstruction ◽  
Dictionary Learning ◽  
Reconstruction Error ◽  
Imaging Method ◽  
L1 Norm ◽  
Norm Minimization ◽  
Imaging Results ◽  
Computational Speed ◽  
Feature Information ◽  
The Relationship

Sparse imaging relies on sparse representations of the target scenes to be imaged. Predefined dictionaries have long been used to transform radar target scenes into sparse domains, but the performance is limited by the artificially designed or existing transforms, e.g., Fourier transform and wavelet transform, which are not optimal for the target scenes to be sparsified. The dictionary learning (DL) technique has been exploited to obtain sparse transforms optimized jointly with the radar imaging problem. Nevertheless, the DL technique is usually implemented in a manner of patch processing, which ignores the relationship between patches, leading to the omission of some feature information during the learning of the sparse transforms. To capture the feature information of the target scenes more accurately, we adopt image patch group (IPG) instead of patch in DL. The IPG is constructed by the patches with similar structures. DL is performed with respect to each IPG, which is termed as group dictionary learning (GDL). The group oriented sparse representation (GOSR) and target image reconstruction are then jointly optimized by solving a l1 norm minimization problem exploiting GOSR, during which a generalized Gaussian distribution hypothesis of radar image reconstruction error is introduced to make the imaging problem tractable. The imaging results using the real ISAR data show that the GDL-based imaging method outperforms the original DL-based imaging method in both imaging quality and computational speed.

scholarly journals A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jianhong Xiang ◽  
Huihui Yue ◽  
Xiangjun Yin ◽  
Linyu Wang
Keyword(s):  
Signal Reconstruction ◽  
Real Data ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Sparse Signal Reconstruction ◽  
System Equation ◽  
Norm Minimization ◽  
Better Than

Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.

scholarly journals A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiehua Zhu ◽  
Xiezhang Li
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Optimization Problems ◽  
Computation Time ◽  
Signal Recovery ◽  
L1 Norm ◽  
Smooth Measure ◽  
Regularization Algorithm ◽  
Total Variation Minimization ◽  
Alternating Direction

The nonmonotone alternating direction algorithm (NADA) was recently proposed for effectively solving a class of equality-constrained nonsmooth optimization problems and applied to the total variation minimization in image reconstruction, but the reconstructed images suffer from the artifacts. Though by the l0-norm regularization the edge can be effectively retained, the problem is NP hard. The smoothed l0-norm approximates the l0-norm as a limit of smooth convex functions and provides a smooth measure of sparsity in applications. The smoothed l0-norm regularization has been an attractive research topic in sparse image and signal recovery. In this paper, we present a combined smoothed l0-norm and l1-norm regularization algorithm using the NADA for image reconstruction in computed tomography. We resolve the computation challenge resulting from the smoothed l0-norm minimization. The numerical experiments demonstrate that the proposed algorithm improves the quality of the reconstructed images with the same cost of CPU time and reduces the computation time significantly while maintaining the same image quality compared with the l1-norm regularization in absence of the smoothed l0-norm.

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