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

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.

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A Rapid Non-Linear Diffusion Compressed Sensing parallel MR Image Reconstruction

Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing ◽

10.1145/3293353.3293411 ◽

2018 ◽

Author(s):

Ajin Joy ◽

Joseph Suresh Paul

Keyword(s):

Image Reconstruction ◽

Compressed Sensing ◽

Mr Image ◽

Linear Diffusion ◽

Non Linear ◽

Mr Image Reconstruction

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Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Applied Sciences ◽

10.3390/app11041435 ◽

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.

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