Adaptive variable step algorithm for missing samples recovery in sparse signals
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| Title: | Adaptive variable step algorithm for missing samples recovery in sparse signals |
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| Authors: | Stefan Vujovic, Milos Dakovic, Ljubisa Stankovic |
| Source: | IET Signal Processing. 8:246-256 |
| Publication Status: | Preprint |
| Publisher Information: | Institution of Engineering and Technology (IET), 2014. |
| Publication Year: | 2014 |
| Subject Terms: | FOS: Computer and information sciences, 13. Climate action, Computer Science - Information Theory, Information Theory (cs.IT), 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology |
| Description: | Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary positioned samples could be analyzed in the same way as compressive sensed signals by omitting the corrupted samples and considering them as unavailable during the recovery process. The reconstruction of missing samples is done by using one of the well known reconstruction algorithms. In this paper we will propose a very simple and efficient adaptive variable step algorithm, applied directly to the concentration measures, without reformulating the reconstruction problem within the standard linear programming form. Direct application of the gradient approach to the nondifferentiable forms of measures lead us to introduce a variable step size algorithm. A criterion for changing adaptive algorithm parameters is presented. The results are illustrated on the examples with sparse signals, including approximately sparse signals and noisy sparse signals. 12 pages, 11 figures, Submitted to IET Signal Processing |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1751-9683 1751-9675 |
| DOI: | 10.1049/iet-spr.2013.0385 |
| DOI: | 10.48550/arxiv.1309.5749 |
| Access URL: | http://arxiv.org/pdf/1309.5749.pdf http://arxiv.org/abs/1309.5749 https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0385 https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-spr.2013.0385 https://ieeexplore.ieee.org/document/6817404 https://dblp.uni-trier.de/db/journals/corr/corr1309.html#StankovicDV13 https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2013.0385 |
| Rights: | Wiley Online Library User Agreement arXiv Non-Exclusive Distribution |
| Accession Number: | edsair.doi.dedup.....d755514a2bc01ef5ec8c79d23f16d0b6 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary positioned samples could be analyzed in the same way as compressive sensed signals by omitting the corrupted samples and considering them as unavailable during the recovery process. The reconstruction of missing samples is done by using one of the well known reconstruction algorithms. In this paper we will propose a very simple and efficient adaptive variable step algorithm, applied directly to the concentration measures, without reformulating the reconstruction problem within the standard linear programming form. Direct application of the gradient approach to the nondifferentiable forms of measures lead us to introduce a variable step size algorithm. A criterion for changing adaptive algorithm parameters is presented. The results are illustrated on the examples with sparse signals, including approximately sparse signals and noisy sparse signals.<br />12 pages, 11 figures, Submitted to IET Signal Processing |
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| ISSN: | 17519683 17519675 |
| DOI: | 10.1049/iet-spr.2013.0385 |