The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation
Abstract
An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ¿ A, where ¿ is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.
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Cites
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$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
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Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Self-citationAtomic Decomposition by Basis Pursuit
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The contourlet transform: an efficient directional multiresolution image representation
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Entropy-based algorithms for best basis selection
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Cited by
Data-driven discovery of coordinates and governing equations
Champion, K., Lusch, B., Kutz, J. N., & Brunton, S. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906995116 · Cited by 909
Predicting Parameters in Deep Learning
Denil, M., Shakibi, B., Dinh, L., Ranzato, M., & de Freitas, N. (2013) · arXiv · doi:10.48550/arxiv.1306.0543 · Cited by 853
Predicting Parameters in Deep Learning
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Metadata
- Authors
- Rubinstein, R., Zibulevsky, M., & Elad, M.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2010-03
- Volume
- 58
- Issue
- 3
- Pages
- 1553-1564
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2099321050
- Cited by
- 616
- References
- 46
- FWCI
- 21.25
- Type
- journal article
- Language
- en
- Open access
- closed
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