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Content Provider | IET Digital Library |
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Author | Sadeghigol, Zahra Kahaei, Mohammad Hossein Haddadi, Frazan |
Abstract | The tree-structured complex wavelet Bayesian compressing sensing (TSCW-BCS) is introduced. The Bessel K form (BKF) probability density function; which has heavy tails out of the origin, is used as the prior. The inter-scale statistical relation between complex wavelet coefficients is modelled by the hidden Markov tree. The Markov chain Monte Carlo inference is obtained based on the BKF and then the posterior parameters of wavelet coefficients are derived. Simulation results show that the proposed TSCW-BCS outperforms many well-known CS methods. |
Starting Page | 412 |
Ending Page | 418 |
Page Count | 7 |
ISSN | 17519675 |
Volume Number | 9 |
e-ISSN | 17519683 |
Issue Number | Issue 5, Jul (2015) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/9/5 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0129 |
Journal | IET Signal Processing |
Publisher Date | 2015-06-08 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Bayes Method Bessel K Form Probability Density Function BKF Probability Density Function Combinatorial Mathematics Complex Wavelet Coefficient Compressed Sensing Computer Vision And Image Processing Technique Hidden Markov Tree Image And Video Coding Inference Mechanisms Integral Transforms Knowledge Engineering Technique Markov Chain Monte Carlo Inference Markov Processes Mathematics Monte Carlo Method Posterior Parameter Tree-structured Complex Wavelet Bayesian Compressing Sensing Tree-structured Complex Wavelet Transform Trees TSCW-BCS Wavelet Transform |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering |
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