Multi-Frame Super-Resolution Image Reconstruction Employing the Novel Estimator L1inv-Norm

dc.contributor.authorPanagiotopoulou, Antigoni
dc.contributor.otherΠαναγιωτοπούλου, Αντιγόνη
dc.date.accessioned2012-09-17T06:09:53Z
dc.date.available2012-09-17T06:09:53Z
dc.date.copyright11-13 April 2012el
dc.date.issued2012-09-17
dc.description.abstractIn multi-frame Super-Resolution (SR) image reconstruction a single High-Resolution (HR) image is created from a sequence of Low-Resolution (LR) frames. This work considers stochastic regularized multi-frame SR image reconstruction from the data-fidelity point of view. In fact, a novel estimator named L1inv-norm is proposed for assuring fidelity to the measured data. This estimator presents the hybrid form of both L1 error norm and logarithm ln. The introduced L1inv-norm is combined with the Bilateral Total Variation (BTV) regularization. The proposed SR method is directly compared with an existing SR method which employs the Lorentzian estimator in combination with the BTV regularizer. The experimental results prove that the proposed technique predominates over the existing technique.el
dc.identifier.urihttp://hdl.handle.net/10889/5442
dc.language.isoenel
dc.relation.ispartof19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012el
dc.subjectSuper-resolutionel
dc.subjectData-fidelityel
dc.subjectHybrid formel
dc.subjectL1 estimatorel
dc.subjectLogarithm lnel
dc.titleMulti-Frame Super-Resolution Image Reconstruction Employing the Novel Estimator L1inv-Normel
dc.typeConference (paper)el
dcmitype.EventInternational Conference on Systems, Signals and Image Processing, IWSSIP 2012el
dcterms.extent562-565el
dcterms.locationVienna, Austriael

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