Model-Based Bayesian Compressive Sensing of Non-stationary Images Using a Wavelet-Domain Triplet Markov Fields Model-Reference-Cited by-同舟云学术

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Model-Based Bayesian Compressive Sensing of Non-stationary Images Using a Wavelet-Domain Triplet Markov Fields Model

Published:2020-07-01 Issue:1 Volume:40 Page:438-465
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Torkamani Razieh,Sadeghzadeh Ramazan-Ali

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-020-01484-w.pdf

Reference61 articles.

1. H. Azami, S. Sanei, K. Mohammadi, H. Hassanpour, A hybrid evolutionary approach to segmentation of non-stationary signals. Elsevier Dig. Signal Process. 23, 1103–1114 (2013). https://doi.org/10.1016/j.dsp.2013.02.019

2. S.D. Babacan, R. Molina, A.K. Katsaggelos, Bayesian compressive sensing using Laplace priors. IEEE Trans. Image Process. 19(1), 53–63 (2010). https://doi.org/10.1109/TIP.2009.2032894

3. R.G. Baraniuk, Compressive sensing. IEEE Signal Process. Mag. (2007). https://doi.org/10.1109/MSP.2007.4286571

4. R.G. Baraniuk, V. Cevher, M.F. Duarte et al., Model-based compressive sensing. IEEE Trans. Inf. Theory 56(4), 1982–2001 (2010). https://doi.org/10.1109/TIT.2010.2040894

5. M.J Beal, Variational Algorithms for approximate Bayesian inference, Ph.D. dissertation, Univ. College of London, London, U.K., 2003

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