Speckle reduction in PolSAR by multi-channel variance stabilization and Gaussian denoising: MuLoG
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DELEDALLE, Charles-Alban | |
| hal.structure.identifier | Laboratoire Hubert Curien [LabHC] | |
| dc.contributor.author | DENIS, L. | |
| hal.structure.identifier | Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES] | |
| hal.structure.identifier | Département Images, Données, Signal [IDS] | |
| dc.contributor.author | TUPIN, Florence | |
| dc.date.accessioned | 2024-04-04T03:05:20Z | |
| dc.date.available | 2024-04-04T03:05:20Z | |
| dc.date.issued | 2018-06 | |
| dc.date.conference | 2018-06 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193236 | |
| dc.description.abstractEn | Due to speckle phenomenon, some form of filtering must be applied to SAR data prior to performing any polarimet-ric analysis. Beyond the simple multilooking operation (i.e., moving average), several methods have been designedspecifically for PolSAR filtering. The specifics of speckle noise and the correlations between polarimetric channelsmake PolSAR filtering more challenging than usual image restoration problems. Despite their striking performance,existing image denoising algorithms, mostly designed for additive white Gaussian noise, cannot be directly applied toPolSAR data. We bridge this gap with MuLoG by providing a general scheme that stabilizes the variance of the po-larimetric channels and that can embed almost any Gaussian denoiser. We describe MuLoG approach and illustrate itsperformance on airborne PolSAR data using a very recent Gaussian denoiser based on a convolutional neural network. | |
| dc.language.iso | en | |
| dc.source.title | EUSAR | |
| dc.subject.en | Speckle filtering | |
| dc.subject.en | multiplicative noise | |
| dc.subject.en | variance stabilization | |
| dc.subject.en | polarimetry | |
| dc.title.en | Speckle reduction in PolSAR by multi-channel variance stabilization and Gaussian denoising: MuLoG | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Informatique [cs]/Traitement des images | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | EUSAR | |
| bordeaux.country | DE | |
| bordeaux.title.proceeding | EUSAR | |
| bordeaux.conference.city | Aachen | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01860234 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.popular | non | |
| hal.audience | Non spécifiée | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01860234v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=EUSAR&rft.date=2018-06&rft.au=DELEDALLE,%20Charles-Alban&DENIS,%20L.&TUPIN,%20Florence&rft.genre=unknown |
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