Hotrakool, W. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2015) An optimal learning parameter for running Gaussian-based referenced compressive sensing. In: 2nd Proceedings of IET International Conference on Intelligent Signal Processing 2015 (ISP). 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), 01-02 Dec 2015, London, UK. IET ISBN 9781785611360
Abstract
One of the approaches to exploit temporal redundancy in compressive sensing reconstruction of spatio-temporal signals is the Running Gaussian-based Referenced Compressive Sensing. It uses the weighted-average of all prior reconstructed instances as a reference to reconstruct the next instance with high accuracy. The performance of this approach depends on the weight called learning parameter. This work studies the relationship between the learning parameter and the reconstruction accuracy. We show that the small value of the learning parameter is more suitable for natural signals with dynamic sparse supports. We also propose a dynamic optimal learning parameter that provides good reconstruction accuracy for all signals. Out experimental results show that the proposed optimal learning parameter outperforms all fixed values of learning parameter in natural video sequences reconstruction.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 IET. This is an author-produced version of a paper subsequently published in Proceedings of 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | compressive sensing; reconstruction; side-information |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Oct 2021 16:38 |
Last Modified: | 20 Oct 2021 16:38 |
Status: | Published |
Publisher: | IET |
Refereed: | Yes |
Identification Number: | 10.1049/cp.2015.1759 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179455 |