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Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise
Faculty of Technical Sciences, University of Novi Sad, Serbia.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)ORCID-id: 0000-0001-7312-8222
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)
2016 (engelsk)Inngår i: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), IEEE, 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Single image super-resolution (SR) reconstructionaims to estimate a noise-free and blur-free high resolution imagefrom a single blurred and noisy lower resolution observation.Most existing SR reconstruction methods assume that noise in theimage is white Gaussian. Noise resulting from photon countingdevices, as commonly used in image acquisition, is, however,better modelled with a mixed Poisson-Gaussian distribution. Inthis study we propose a single image SR reconstruction methodbased on energy minimization for images degraded by mixedPoisson-Gaussian noise.We evaluate performance of the proposedmethod on synthetic images, for different levels of blur andnoise, and compare it with recent methods for non-Gaussiannoise. Analysis shows that the appropriate treatment of signaldependentnoise, provided by our proposed method, leads tosignificant improvement in reconstruction performance.

sted, utgiver, år, opplag, sider
IEEE, 2016.
Serie
International Conference on Image Processing Theory Tools and Applications, E-ISSN 2154-512X
Emneord [en]
super-resolution, image zooming, signal dependent noise, energy minimization, variance stabilizing transform, total variation.
HSV kategori
Forskningsprogram
Datoriserad bildbehandling; Datoriserad bildanalys
Identifikatorer
URN: urn:nbn:se:uu:diva-308095DOI: 10.1109/IPTA.2016.7820962ISI: 000393589800014ISBN: 978-1-4673-8910-5 (digital)OAI: oai:DiVA.org:uu-308095DiVA, id: diva2:1049191
Konferanse
The 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland
Forskningsfinansiär
VINNOVATilgjengelig fra: 2016-11-23 Laget: 2016-11-23 Sist oppdatert: 2018-08-24bibliografisk kontrollert

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Totalt: 949 treff
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