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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
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. (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. (Centre for Image Analysis)
2016 (English)In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, 2016, 123-127 p.Conference paper (Refereed)
Abstract [en]

Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.

Place, publisher, year, edition, pages
IEEE, 2016. 123-127 p.
Keyword [en]
Image restoration, Minimization, Estimation, Transmission electron microscopy, Noise measurement, PSNR, 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-308086DOI: 10.1109/ISBI.2016.7493226OAI: oai:DiVA.org:uu-308086DiVA: diva2:1049176
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23

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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Computer Vision and Robotics (Autonomous Systems)

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