Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

Ju Wang; Yin Liu; Wei Juan Zhang; Kun Li

doi:10.4028/www.scientific.net/AMM.599-601.1453

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601Compressed Sensing Image Reconstruction Based on...

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

Abstract:

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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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1453-1456

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1453

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Online since:

August 2014

Authors:

Ju Wang*, Yin Liu, Wei Juan Zhang, Kun Li

Keywords:

Compressed Sensing, Image Reconstruction, K-Mean, Particle Swarm Optimization (PSO), Sparse Decomposition

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* - Corresponding Author

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