Denoising Smooth Signals Using a Bayesian Approach: Application to Altimetry
| Autor: | Paul Honeine, Abderrahim Halimi, Stephen McLaughlin, Gerald S. Buller |
|---|---|
| Přispěvatelé: | Heriot-Watt University [Edinburgh] (HWU), Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Honeine, Paul |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
denoising quality signal denoising Gaussian Bayesian inference posterior distribution coordinate descent algorithm (CDA) gamma Markov random fields (gamma-MRFs) Geophysics. Cosmic physics 0211 other engineering and technologies 02 engineering and technology computer.software_genre gamma Markov random field altimetric parameter quality 0202 electrical engineering electronic engineering information engineering Coordinate descent Noise reduction state-of-the-art algorithms computational cost Mathematics synthetic signal Markov random field Noise (signal processing) Estimation theory Markov processes Computational modeling Bayes methods denoising smooth signals Correlation Ocean engineering height measurement random processes symbols 020201 artificial intelligence & image processing smooth evolution Altimetry parameter estimation Algorithm Bayesian strategy [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing signal energies noise variances Satellites Posterior probability Machine learning Gaussian noise Bayesian algorithm symbols.namesake [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] satellite altimetric data Computers in Earth Sciences smooth signals estimation TC1501-1800 [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing 021101 geological & geomatics engineering business.industry QC801-809 Bayesian approach fast coordinate descent algorithm statistical distributions Logic gates [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] noise Gaussian properties real signal smoothing methods continuous signals classification strategy Artificial intelligence business Signal processing algorithms computer successive signals |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 10, Iss 4, Pp 1278-1289 (2017) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2017, 10 (4), pp.1278-1289 |
| ISSN: | 2151-1535 1939-1404 |
| Popis: | This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The proposed Bayesian model takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. In comparison with state-of-the-art algorithms, the proposed strategy provides a good compromise between denoising quality and necessary reduced computational cost. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation or a classification strategy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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