The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing
Abstract
Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and, therefore, this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.
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Cites
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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An Introduction To Compressive Sampling
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Cited by
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Zhang, J., & Ghanem, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00196 · Cited by 1,309
Model-Based Compressive Sensing
Baraniuk, R. G., Cevher, V., Duarte, M. F., & Hegde, C. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2040894 · Cited by 1,308
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
Yang, Y., Sun, J., Li, H., & Xu, Z. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2883941 · Cited by 908
Bayesian Compressive Sensing Via Belief Propagation
Baron, D., Sarvotham, S., & Baraniuk, R. G. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2027773 · Cited by 498
Structured Variable Selection with Sparsity-Inducing Norms
Jenatton, R., Audibert, J.-Y., & Bach, F. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0904.3523 · Cited by 431
Metadata
- Authors
- He, L., & Carin, L.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2009-09
- Volume
- 57
- Issue
- 9
- Pages
- 3488-3497
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2049502219
- Cited by
- 506
- References
- 45
- FWCI
- 43.51
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
