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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 University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)
2016 (English)In: 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland, IEEE, 2016Conference paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2016.
Keyword [en]
super-resolution, image zooming, signal dependent noise, energy minimization, variance stabilizing transform, total variation.
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-308095OAI: oai:DiVA.org:uu-308095DiVA: diva2:1049191
Conference
The 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland
Funder
VINNOVA
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23

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