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Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study
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: Journal of Electronic Imaging, ISSN 1017-9909, Vol. 25, no 4, 043020Article in journal (Refereed) Published
Abstract [en]

Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson–Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.

Place, publisher, year, edition, pages
2016. Vol. 25, no 4, 043020
Keyword [en]
image restoration; Poisson noise; mixed Poisson–Gaussian noise; variance stabilizing transform; total variation; Huber potential function
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-308093OAI: oai:DiVA.org:uu-308093DiVA: diva2:1049186
Funder
Swedish Research CouncilVINNOVA
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23

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http://dx.doi.org/10.1117/1.JEI.25.4.043020
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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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