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  Denoising sparse noise via online dictionary learning

Cherian, A., Sra, S., & Papanikolopoulos, N. (2011). Denoising sparse noise via online dictionary learning. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 2060-2063). Piscataway, NJ, USA: IEEE.

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Cherian, A, Author
Sra, S1, 2, Author           
Papanikolopoulos, N, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the most successful. But many state-of-the-art denoising techniques inherently assume that the signal noise is Gaussian. We instead propose to learn overcomplete dictionaries where the signal is allowed to have both Gaussian and (sparse) Laplacian noise. Dictionary learning in this setting leads to a difficult non-convex optimization problem, which is further exacerbated by large input datasets. We tackle these difficulties by developing an efficient online algorithm that scales to data size. To assess the efficacy of our model, we apply it to dictionary learning for data that naturally satisfy our noise model, namely, Scale Invariant Feature Transform (SIFT) descriptors. For these data, we measure performance of the learned dictionary on the task of nearest-neighbor retrieval: compared to methods that do not explicitly model sparse noise our method exhibits superior performance.

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 Dates: 2011-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/ICASSP.2011.5946730
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Title: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)
Place of Event: Praha, Czech Republic
Start-/End Date: 2011-05-22 - 2011-05-27

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Title: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2060 - 2063 Identifier: ISBN: 978-1-4577-0539-7