An optimal learning parameter for running Gaussian-based referenced compressive
An optimal learning parameter for running Gaussian-based referenced compressive
- Author(s): W. Hotrakool and C. Abhayaratne
- DOI: 10.1049/cp.2015.1759
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- Author(s): W. Hotrakool and C. Abhayaratne Source: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), 2015 page ()
- Conference: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)
- DOI: 10.1049/cp.2015.1759
- ISBN: 978-1-78561-136-0
- Location: London, UK
- Conference date: 1-2 Dec. 2015
- Format: PDF
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.
Inspec keywords: Gaussian processes; compressed sensing; image sequences; learning (artificial intelligence); image reconstruction; video coding; spatiotemporal phenomena
Subjects: Other topics in statistics; Video signal processing; Image and video coding; Knowledge engineering techniques; Other topics in statistics; Computer vision and image processing techniques
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