Mostrar el registro sencillo del ítem

Single-Image based unsupervised joint segmentation and denoising

hal.structure.identifierDepartment of Mathematics [Innsbruck]
dc.contributor.authorGRUBER, Nadja
hal.structure.identifierMRC Laboratory of Molecular Biology [Cambridge, UK] [LMB]
dc.contributor.authorSCHWAB, Johannes
hal.structure.identifierInstitut Pascal [IP]
dc.contributor.authorDEBROUX, Noémie
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierDepartment of Mathematics [Innsbruck]
dc.contributor.authorHALTMEIER, Markus
dc.date.accessioned2024-04-04T02:33:14Z
dc.date.available2024-04-04T02:33:14Z
dc.date.issued2023-09-19
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190457
dc.description.abstractEnIn this work, we develop an unsupervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of a variational segmentation method with the power of a self-supervised, single-image based deep learning approach. One major strength of our method lies in the fact, that in contrast to data-driven methods, where huge amounts of labeled samples are necessary, our model can segment an image into multiple meaningful regions without any training database. Further, we introduce a novel energy functional in which denoising and segmentation are coupled in a way that both tasks benefit from each other. The limitations of existing single-image based variational segmentation methods, which are not capable of dealing with high noise or generic texture, are tackled by this specific combination with self-supervised image denoising. We propose a unified optimisation strategy and show that, especially for very noisy images available in microscopy, our proposed joint approach outperforms its sequential counterpart as well as alternative methods focused purely on denoising or segmentation. Another comparison is conducted with a supervised deep learning approach designed for the same application, highlighting the good performance of our approach.
dc.language.isoen
dc.title.enSingle-Image based unsupervised joint segmentation and denoising
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Traitement des images
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.identifier.arxiv2309.10511
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-04212910
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04212910v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-09-19&rft.au=GRUBER,%20Nadja&SCHWAB,%20Johannes&DEBROUX,%20No%C3%A9mie&PAPADAKIS,%20Nicolas&HALTMEIER,%20Markus&rft.genre=preprint


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem