The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Bayesian Compressive Sensing Via Belief Propagation
Open accessAbstract
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length-N signal containingKlarge coefficients, our CS-BP decoding algorithm uses O(K log(N)) measurements and O(N log2(N)) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models.
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Cites
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Dai, W., & Milenkovic, O. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2016006 · Cited by 2,388
Computational Methods for Sparse Solution of Linear Inverse Problems
Tropp, J. A., & Wright, S. J. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2044010 · Cited by 1,046
Statistical physics of inference: thresholds and algorithms
Zdeborová, L., & Krzakala, F. (2016) · Advances in Physics · doi:10.1080/00018732.2016.1211393 · Cited by 440
Compressive fluorescence microscopy for biological and hyperspectral imaging
Studer, V., Bobin, J., Chahid, M., Mousavi, H. S., Candes, E., & Dahan, M. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1119511109 · Cited by 411
Fast $\ell_{1}$-Minimization Algorithms for Robust Face Recognition
Yang, A. Y., Zhou, Z., Balasubramanian, A. G., Sastry, S. S., & Ma, Y. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2262292 · Cited by 341
Metadata
- Authors
- Baron, D., Sarvotham, S., & Baraniuk, R. G.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2010-01
- Volume
- 58
- Issue
- 1
- Pages
- 269-280
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2135859872
- Cited by
- 498
- References
- 55
- FWCI
- 56.32
- Type
- journal article
- Language
- en
- Open access
- green
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