Learning local regularization for variational image restoration
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PROST, Jean | |
| dc.contributor.author | HOUDARD, Antoine | |
| hal.structure.identifier | Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145] | |
| dc.contributor.author | ALMANSA, Andrés | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PAPADAKIS, Nicolas | |
| dc.contributor.editor | Springer | |
| dc.date.accessioned | 2024-04-04T02:47:18Z | |
| dc.date.available | 2024-04-04T02:47:18Z | |
| dc.date.created | 2021-02-11 | |
| dc.date.issued | 2021-04-30 | |
| dc.date.conference | 2021-05-17 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191629 | |
| dc.description.abstractEn | In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. This regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. The regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. This yields a regularization function that can be incorporated in any image restoration problem. The efficiency of the framework is finally shown on denoising and deblurring applications. | |
| dc.description.sponsorship | Repenser la post-production d'archives avec des méthodes à patch, variationnelles et par apprentissage - ANR-19-CE23-0027 | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.publisher | Springer International Publishing | |
| dc.publisher.location | Cham | |
| dc.subject.en | image inverse problem | |
| dc.subject.en | denoising | |
| dc.subject.en | deblurring | |
| dc.title.en | Learning local regularization for variational image restoration | |
| dc.type | Communication dans un congrès | |
| dc.identifier.doi | 10.1007/978-3-030-75549-2_29 | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
| dc.subject.hal | Statistiques [stat]/Machine Learning [stat.ML] | |
| dc.identifier.arxiv | 2102.06155 | |
| bordeaux.page | 358-370 | |
| bordeaux.volume | LNCS 12679 | |
| 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 | International Conference on Scale Space and Variational Methods in Computer Vision (SSVM'21) | |
| bordeaux.country | FR | |
| bordeaux.conference.city | Cabourg | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-03139784 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2021-05-19 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03139784v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021-04-30&rft.volume=LNCS%2012679&rft.spage=358-370&rft.epage=358-370&rft.au=PROST,%20Jean&HOUDARD,%20Antoine&ALMANSA,%20Andr%C3%A9s&PAPADAKIS,%20Nicolas&rft.genre=unknown |
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