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2015 | OriginalPaper | Chapter

4. Centralized Joint Recovery

Authors : Giulio Coluccia, Chiara Ravazzi, Enrico Magli

Published in: Compressed Sensing for Distributed Systems

Publisher: Springer Singapore

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Abstract

This chapter surveys the basic concepts and algorithms for joint reconstruction from compressive measurements in a network of nodes. The nodes acquire measurements of a set of signals obeying a specific joint sparsity model, while a centralized fusion center collects the measurements of the entire network and jointly processes them to reconstruct the acquired signals.

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previous chapter Rate-Distortion Theory of Distributed Compressed Sensing
next chapter Distributed Recovery
Literature
1.
Baron, D., Duarte, M.F., Wakin, M.B., Sarvotham, S., Baraniuk, R.G.: Distributed compressive sensing. arXiv preprint arXiv:​0901.​3403 (2009)
2.
Mishali, M., Eldar, Y.C.: Reduce and boost: recovering arbitrary sets of jointly sparse vectors. IEEE Trans. Signal Process. 56(10), 4692–4702 (2008)CrossRefMathSciNet
3.
Fornasier, M., Rauhut, H.: Recovery algorithms for vector-valued data with joint sparsity constraints. SIAM J. Numer. Anal. 46(2), 577–613 (2008)CrossRefMATHMathSciNet
4.
Hormati, A., Vetterli, M.: Distributed compressed sensing: sparsity models and reconstruction algorithms using annihilating filter. In: IEEE International Conference on Acoustics, Speech and Signal Processing 2008, pp. 5141–5144. ICASSP 2008, IEEE (2008)
5.
Coluccia, G., Kuiteing, S.K., Abrardo, A., Barni, M., Magli, E.: Progressive compressed sensing and reconstruction of multidimensional signals using hybrid transform/prediction sparsity model. IEEE J. Emerg. Sel. Top. Circuits Syst. 2(3), 340–352 (2012)CrossRef
6.
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655–4666 (2007)CrossRefMATHMathSciNet
7.
Wakin, M.B., Duarte, M.F., Sarvotham, S., Baron, D., Baraniuk, R.G.: Recovery of jointly sparse signals from few random projections. In: NIPS (2005)
8.
Xu, W., Lin, J., Niu, K., He, Z.: A joint recovery algorithm for distributed compressed sensing. Trans. Emerg. Telecommun. Technol. 23(6), 550–559 (2012)CrossRef
9.
Davenport, M.A., Boufounos, P.T., Baraniuk, R.G.: Compressive domain interference cancellation. Technical report, DTIC Document (2009)
10.
Schnelle, S., Laska, J., Hegde, C., Duarte, M., Davenport, M., Baraniuk, R.: Texas hold ’em algorithms for distributed compressive sensing. In: IEEE ICASSP. pp. 2886–2889 (2010)
11.
Stankovic, V., Stankovic, L., Cheng, S.: Compressive image sampling with side information. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3037–3040 (2009)
12.
Coluccia, G., Magli, E., Roumy, A., Toto-Zarasoa, V., et al.: Lossy compression of distributed sparse sources: a practical scheme. In: 2011 European Signal Processing Conference (EUSIPCO) (2011)
13.
Valsesia, D., Coluccia, G., Magli, E.: Joint recovery algorithms using difference of innovations for distributed compressed sensing. In: 2013 Asilomar Conference on Signals, Systems and Computers, IEEE, pp. 414–417 (2013)
Metadata
Title
Centralized Joint Recovery
Authors
Giulio Coluccia
Chiara Ravazzi
Enrico Magli
Copyright Year
2015
Publisher
Springer Singapore
Book
Compressed Sensing for Distributed Systems

Print ISBN: 978-981-287-389-7

Electronic ISBN: 978-981-287-390-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-981-287-390-3_4