The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Dictionaries for Sparse Representation Modeling
Abstract
Sparse and redundant representation modeling of data assumes an ability to describe signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the choice of the dictionary that sparsifies the signals is crucial for the success of this model. In general, the choice of a proper dictionary can be done using one of two ways: i) building a sparsifying dictionary based on a mathematical model of the data, or ii) learning a dictionary to perform best on a training set. In this paper we describe the evolution of these two paradigms. As manifestations of the first approach, we cover topics such as wavelets, wavelet packets, contourlets, and curvelets, all aiming to exploit 1-D and 2-D mathematical models for constructing effective dictionaries for signals and images. Dictionary learning takes a different route, attaching the dictionary to a set of examples it is supposed to serve. From the seminal work of Field and Olshausen, through the MOD, the K-SVD, the Generalized PCA and others, this paper surveys the various options such training has to offer, up to the most recent contributions and structures.
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Cites
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Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2235192 · Cited by 4,421
Nonlocally Centralized Sparse Representation for Image Restoration
Dong, W., Zhang, L., Shi, G., & Li, X. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235847 · Cited by 1,505
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379
Vertex-frequency analysis on graphs
Shuman, D. I., Ricaud, B., & Vandergheynst, P. (2016) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2015.02.005 · Cited by 349
Xu, S., Ji, L., Wang, Z., Li, P., Sun, K., Zhang, C., & Zhang, J. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.3039564 · Cited by 47
Metadata
- Authors
- Rubinstein, R., Bruckstein, A. M., & Elad, M.
- Venue
- Proceedings of the IEEE
- Published
- 2010-06
- Volume
- 98
- Issue
- 6
- Pages
- 1045-1057
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2163398148
- Cited by
- 1,306
- References
- 9
- FWCI
- 60.64
- Type
- journal article
- Language
- en
- Open access
- closed
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