Novel Optimization Method for ProjectionMatrix in Compress Sensing Theory

Wu Guang-wen; Zhang Ai-jun; Wang Chang-ming

doi:10.11999/JEIT141450

Volume 37 Issue 7

Jul. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(7): 1681-1687

Wu Guang-wen, Zhang Ai-jun, Wang Chang-ming. Novel Optimization Method for ProjectionMatrix in Compress Sensing Theory[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1681-1687. doi: 10.11999/JEIT141450

Citation:

Wu Guang-wen, Zhang Ai-jun, Wang Chang-ming. Novel Optimization Method for ProjectionMatrix in Compress Sensing Theory[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1681-1687. doi: 10.11999/JEIT141450

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Novel Optimization Method for ProjectionMatrix in Compress Sensing Theory

doi: 10.11999/JEIT141450

1.
2.
(School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Received Date: 2014-11-20
Rev Recd Date: 2015-02-11
Publish Date: 2015-07-19

Abstract

Abstract

Considering the influence of the projection matrix on Compressed Censing (CS), a novel method is proposed to optimize the projection matrix. In order to improve the signals reconstruction precise and the stability of the optimization algorithm of the projection matrix, the proposed method adopts a differentiable threshold function to shrink the off-diagonal items of a Gram matrix corresponding to the mutual coherence between the projection matrix and sparse dictionary, and introduces a gradient descent approach based on the Wolfs-conditions to solve the optimization projection matrix. The Basis-Pursuit (BP) algorithm and the Orthogonal Matching Pursuit (OMP) algorithm are applied to find the solution of the minimuml0-norm optimization issue and the compressed sensing are utilized to sense and reconstruct the random vectors, wavelets noise test signals and pictures. The results of the simulation show the proposed method based on the projection matrix optimization is able to improve the quality of the reconstruction performance.
- Compressed Sensing (CS),
- Mutual coherence,
- Basis-Pursuit (BP) algorithm,
- Orthogonal Matching- Pursuit (OMP) algorithm

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References(19)

References

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