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Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise
Univ Novi Sad, Fac Tech Sci, Novi Sad, Serbia.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.ORCID iD: 0000-0001-7312-8222
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.ORCID iD: 0000-0002-6041-6310
2019 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 28, no 1, article id 013046Article in journal (Refereed) Published
Abstract [en]

We present a segmentation method that estimates the relative coverage of each pixel in a sensed image by each image component. The proposed super-resolution blur-aware model (utilizes a priori knowledge of the image blur) for linear unmixing of image intensities relies on a sparsity promoting approach expressed by two main requirements: (i) minimization of Huberized total variation, providing smooth object boundaries and noise removal, and (ii) minimization of nonedge image fuzziness, responding to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. Edge fuzziness due to partial coverage is allowed, enabling subpixel precise feature estimates. The segmentation is formulated as an energy minimization problem and solved by the spectral projected gradient method, utilizing a graduated nonconvexity scheme. Quantitative and qualitative evaluation on synthetic and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement. (C) 2019 SPIE and IS&T

Place, publisher, year, edition, pages
IS&T & SPIE , 2019. Vol. 28, no 1, article id 013046
Keywords [en]
fuzzy segmentation, super-resolution, deconvolution, linear unmixing, total variation, energy minimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-379780DOI: 10.1117/1.JEI.28.1.013046ISI: 000460119700046OAI: oai:DiVA.org:uu-379780DiVA, id: diva2:1297782
Funder
Swedish Research Council, 2014-4231Swedish Research Council, 2015-05878Swedish Research Council, 2017-04385Available from: 2019-03-21 Created: 2019-03-21 Last updated: 2019-03-21Bibliographically approved

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Lindblad, JoakimSladoje, Natasa

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