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Joint Image Deconvolution and Separation Using Mixed Dictionaries
Azarbaijan Shahid Madani Univ, Dept Appl Math, Tabriz 5375171379, Iran..
Azarbaijan Shahid Madani Univ, Dept Appl Math, Tabriz 5375171379, Iran..
Azarbaijan Shahid Madani Univ, Dept Appl Math, Tabriz 5375171379, Iran..
KTH, Skolan för teknikvetenskap (SCI), Tillämpad fysik, Biofysik. KTH, Centra, Science for Life Laboratory, SciLifeLab. SciLifeLab, Adv Light Microscopy Facil, S-17165 Solna, Sweden..ORCID-id: 0000-0003-0578-4003
Vise andre og tillknytning
2019 (engelsk)Inngår i: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, nr 8, s. 3936-3945Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The task of separating an image into distinct components that represent different features plays an important role in many applications. Traditionally, such separation techniques are applied once the image in question has been reconstructed from measured data. We propose an efficient iterative algorithm, where reconstruction is performed jointly with the task of separation. A key assumption is that the image components have different sparse representations. The algorithm is based on a scheme that minimizes a functional composed of a data discrepancy term and the l(1)-norm of the coefficients of the different components with respect to their corresponding dictionaries. The performance is demonstrated for joint 2D deconvolution and separation into curve- and point-like components, and tests are performed on synthetic data as well as experimental stimulated emission depletion and confocal microscopy data. Experiments show that such a joint approach outperforms a sequential approach, where one first deconvolves data and then applies image separation.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 28, nr 8, s. 3936-3945
Emneord [en]
Inverse problems, image separation, sparse recovery, curvelets, wavelets
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Identifikatorer
URN: urn:nbn:se:kth:diva-255299DOI: 10.1109/TIP.2019.2903316ISI: 000472609200006PubMedID: 30843839Scopus ID: 2-s2.0-85067800119OAI: oai:DiVA.org:kth-255299DiVA, id: diva2:1339541
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QC 20190730

Tilgjengelig fra: 2019-07-30 Laget: 2019-07-30 Sist oppdatert: 2019-07-30bibliografisk kontrollert

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Brismar, HjalmarÖktem, Ozan

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