Image reconstruction of photoacoustic tomography based on finite-aperture-effect corrected compressed sensing algorithm

2014 ◽  
Author(s):  
Chien-Hao Chiu ◽  
Yen Chuo ◽  
Meng-Lin Li
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Photoacoustic Tomography ◽  
Finite Aperture ◽  
Aperture Effect

scholarly journals Model-based correction of finite aperture effect in photoacoustic tomography

2010 ◽  
Vol 18 (25) ◽  
pp. 26285 ◽  
Author(s):  
Meng-Lin Li ◽  
Yi-Chieh Tseng ◽  
Chung-Chih Cheng
Keyword(s):  
Photoacoustic Tomography ◽  
Model Based ◽  
Finite Aperture ◽  
Aperture Effect

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

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 IMAGE RECONSTRUCTION FROM HIGHLY SPARSE AND LIMITED ANGULAR DIFFRACTION TOMOGRAPHY USING COMPRESSED SENSING APPROACH

2017 ◽  
Vol 158 ◽  
pp. 21-36 ◽  
Author(s):  
Pavel Roy Paladhi ◽  
Amin Tayebi ◽  
Portia Banerjee ◽  
Lalita Udpa ◽  
Satish Udpa
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Diffraction Tomography ◽  
Angular Diffraction
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