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Super-resolution imaging

Author: Peyman Milanfar
Publisher: Boca Raton, Fla. : CRC ; London : Taylor & Francis [distributor], 2010.
Series: Digital imaging and computer vision, 1
Edition/Format:   Print book : English
Summary:

Offers an overview of the field of super-resolution imaging. Surveying the techniques in super-resolution imaging, this title provides coverage of the implementations and applications of  Read more...

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Document Type: Book
All Authors / Contributors: Peyman Milanfar
ISBN: 9781439819302 1439819300
OCLC Number: 636915830
Description: pages.
Contents: Image Super-Resolution: Historical Overview and Future Challenges, J. Yang and T. HuangIntroduction to Super-ResolutionNotationsTechniques for Super-ResolutionChallenge issues for Super-ResolutionSuper-Resolution Using Adaptive Wiener Filters, R.C. HardieIntroductionObservation ModelAWF SR AlgorithmsExperimental ResultsConclusionsAcknowledgmentsLocally Adaptive Kernel Regression for Space-Time Super-Resolution, H. Takeda and P. MilanfarIntroductionAdaptive Kernel RegressionExamplesConclusionAppendiXSuper-Resolution With Probabilistic Motion Estimation, M. Protter and M. EladIntroductionClassic Super-Resolution: BackgroundThe Proposed AlgorithmExperimental ValidationSummarySpatially Adaptive Filtering as Regularization in Inverse Imaging, A. Danielyan, A. Foi, V. Katkovnik, and K. EgiazarianIntroductionIterative filtering as regularizationCompressed sensingSuper-resolutionConclusionsRegistration for Super-Resolution, P. Vandewalle, L. Sbaiz, and M. VetterliCamera ModelWhat Is Resolution?Super-Resolution as a Multichannel Sampling ProblemRegistration of Totally Aliased SignalsRegistration of Partially Aliased SignalsConclusionsTowards Super-Resolution in the Presence of Spatially Varying Blur, M. Sorel, F. Sroubek and J. FlusserIntroductionDefocus and Optical AberrationsCamera Motion BlurScene MotionAlgorithmsConclusion AcknowledgmentsToward Robust Reconstruction-Based Super-Resolution, M. Tanaka and M. OkutomiIntroductionOverviewsRobust SR Reconstruction with Pixel SelectionRobust Super-Resolution Using MPEG Motion VectorsRobust Registration for Super-ResolutionConclusionsMulti-Frame Super-Resolution from a Bayesian Perspective, L. Pickup, S. Roberts, A. Zisserman and D. CapelThe Generative ModelWhere Super-Resolution Algorithms Go WrongSimultaneous Super-ResolutionBayesian MarginalizationConcluding RemarksVariational Bayesian Super Resolution Reconstruction, S. Derin Babacan, R. Molina, and A.K. KatsaggelosIntroductionProblem FormulationBayesian Framework for Super ResolutionBayesian InferenceVariational Bayesian Inference Using TV Image PriorsExperimentsEstimation of Motion and BlurConclusionsAcknowledgementsPattern Recognition Techniques for Image Super-Resolution, K. Ni and T.Q. NguyenIntroductionNearest Neighbor Super-ResolutionMarkov Random Fields and ApproximationsKernel Machines for Image Super-ResolutionMultiple Learners and Multiple RegressionsDesign Considerations and ExamplesRemarksGlossarySuper-Resolution Reconstruction of Multi-Channel Images, O.G. Sezer and Y. AltunbasakIntroduction NotationImage Acquisition ModelSubspace RepresentationReconstruction AlgorithmExperiments & DiscussionsConclusionNew Applications of Super-Resolution in Medical Imaging, M.D.Robinson, S.J. Chiu, C.A. Toth, J.A. Izatt, J.Y. Lo, and S. FarsiuIntroductionThe Super-Resolution Framework New Medical Imaging ApplicationsConclusionAcknowledgmentPracticing Super-Resolution: What Have We Learned? N. BozinovicAbstractIntroductionMotionDSP: History and ConceptsMarkets and ApplicationsTechnologyResultsLessons LearnedConclusions
Series Title: Digital imaging and computer vision, 1
Responsibility: edited by Peyman Milanfar.

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