A new adaptive video SRR algorithm with improved robustness to innovations. Borsoi, R. A., Costa, G. H., & Bermudez, J. C. M. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1505-1509, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed. By studying the proximal point cost function representation of the R-LMS iterative equation, a better understanding of its performance is attained, which allows us to devise a new algorithm with improved robustness, while maintaining comparable quality and computational cost. Monte Carlo simulation results illustrate that the proposed method outperforms the traditional and regularized versions of the LMS algorithm.
@InProceedings{8081460,
author = {R. A. Borsoi and G. H. Costa and J. C. M. Bermudez},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {A new adaptive video SRR algorithm with improved robustness to innovations},
year = {2017},
pages = {1505-1509},
abstract = {In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed. By studying the proximal point cost function representation of the R-LMS iterative equation, a better understanding of its performance is attained, which allows us to devise a new algorithm with improved robustness, while maintaining comparable quality and computational cost. Monte Carlo simulation results illustrate that the proposed method outperforms the traditional and regularized versions of the LMS algorithm.},
keywords = {image reconstruction;image resolution;iterative methods;least mean squares methods;Monte Carlo methods;video signal processing;LMS algorithm;adaptive video SRR algorithm;video super-resolution reconstruction method;proximal point cost function representation;R-LMS iterative equation;Monte Carlo simulation;Signal processing algorithms;Robustness;Image reconstruction;Technological innovation;Cost function;Image resolution;Computational efficiency;Super-resolution;R-LMS;outliers},
doi = {10.23919/EUSIPCO.2017.8081460},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343669.pdf},
}Downloads: 0
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