From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling
hal.structure.identifier | Laboratoire Hubert Curien [LabHC] | |
dc.contributor.author | DENIS, Loïc | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | DELEDALLE, Charles-Alban | |
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 | 2024-04-04T02:47:40Z | |
dc.date.available | 2024-04-04T02:47:40Z | |
dc.date.conference | 2019-07-28 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191667 | |
dc.description.abstractEn | Speckle reduction has benefited from the recent progress in image processing, in particular patch-based non-local filtering and deep learning techniques. These two families of methods offer complementary characteristics but have not yet been combined. We explore strategies to make the most of each approach. | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.source.title | IGARSS | |
dc.subject.en | patches | |
dc.subject.en | non-local filtering | |
dc.subject.en | deep learning | |
dc.subject.en | speckle | |
dc.subject.en | SAR | |
dc.subject.en | PolSAR | |
dc.title.en | From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling | |
dc.type | Communication dans un congrès | |
dc.identifier.doi | 10.1109/IGARSS.2019.8898473 | |
dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
dc.subject.hal | Informatique [cs] | |
dc.subject.hal | Informatique [cs]/Apprentissage [cs.LG] | |
dc.subject.hal | Informatique [cs]/Traitement des images | |
bordeaux.page | 5113-5116 | |
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 | 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) | |
bordeaux.country | JP | |
bordeaux.title.proceeding | IGARSS | |
bordeaux.conference.city | Yokohama | |
bordeaux.peerReviewed | oui | |
hal.identifier | ujm-03114605 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | oui | |
hal.conference.end | 2019-08-02 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//ujm-03114605v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=IGARSS&rft.spage=5113-5116&rft.epage=5113-5116&rft.au=DENIS,%20Lo%C3%AFc&DELEDALLE,%20Charles-Alban&TUPIN,%20Florence&rft.genre=unknown |
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