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Efficient Posterior Sampling For Diverse Super-Resolution with Hierarchical VAE Prior

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPROST, Jean
hal.structure.identifierUbisoft
dc.contributor.authorHOUDARD, Antoine
hal.structure.identifierMathématiques Appliquées Paris 5 [MAP5 - UMR 8145]
dc.contributor.authorALMANSA, Andrés
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorPAPADAKIS, Nicolas
dc.date.accessioned2024-04-04T02:30:47Z
dc.date.available2024-04-04T02:30:47Z
dc.date.conference2024-02-27
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190266
dc.description.abstractEnWe investigate the problem of producing diverse solutions to an image super-resolution problem.From a probabilistic perspective, this can be done by sampling from the posterior distribution of an inverse problem, which requires the definition of a prior distribution on the high-resolution images. In this work, we propose to use a pretrained hierarchical variational autoencoder (HVAE) as a prior. We train a lightweight stochastic encoder to encode low-resolution images in the latent space of a pretrained HVAE. At inference, we combine the low-resolution encoder and the pretrained generative model to super-resolve an image. We demonstrate on the task of face super-resolution that our method provides an advantageous trade-off between the computational efficiency of conditional normalizing flows techniques and the sample quality of diffusion based methods.
dc.description.sponsorshipRepenser la post-production d'archives avec des méthodes à patch, variationnelles et par apprentissage - ANR-19-CE23-0027
dc.language.isoen
dc.title.enEfficient Posterior Sampling For Diverse Super-Resolution with Hierarchical VAE Prior
dc.typeCommunication dans un congrès
dc.identifier.doi10.5220/0012352800003660
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
dc.identifier.arxiv2205.10347v4
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleVISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
bordeaux.countryIT
bordeaux.conference.cityRome
bordeaux.peerReviewedoui
hal.identifierhal-03675314
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2024-02-29
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03675314v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=PROST,%20Jean&HOUDARD,%20Antoine&ALMANSA,%20Andr%C3%A9s&PAPADAKIS,%20Nicolas&rft.genre=unknown


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