File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Superresolution Image Reconstruction from Blurred Observations by Multisensors

TitleSuperresolution Image Reconstruction from Blurred Observations by Multisensors
Authors
Issue Date2003
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/37666
Citation
International Journal Of Imaging Systems And Technology, 2003, v. 13 n. 3, p. 153-160 How to Cite?
AbstractSuperresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm. © 2003 Wiley Periodicals, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/156117
ISSN
2015 Impact Factor: 0.571
2015 SCImago Journal Rankings: 0.620
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_US
dc.contributor.authorNg, MKen_US
dc.contributor.authorSze, KNen_US
dc.contributor.authorYau, ACen_US
dc.date.accessioned2012-08-08T08:40:28Z-
dc.date.available2012-08-08T08:40:28Z-
dc.date.issued2003en_US
dc.identifier.citationInternational Journal Of Imaging Systems And Technology, 2003, v. 13 n. 3, p. 153-160en_US
dc.identifier.issn0899-9457en_US
dc.identifier.urihttp://hdl.handle.net/10722/156117-
dc.description.abstractSuperresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm. © 2003 Wiley Periodicals, Inc.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/37666en_US
dc.relation.ispartofInternational Journal of Imaging Systems and Technologyen_US
dc.titleSuperresolution Image Reconstruction from Blurred Observations by Multisensorsen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1002/ima.10053en_US
dc.identifier.scopuseid_2-s2.0-0242272600en_US
dc.identifier.hkuros88729-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0242272600&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume13en_US
dc.identifier.issue3en_US
dc.identifier.spage153en_US
dc.identifier.epage160en_US
dc.identifier.isiWOS:000186085100001-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridNg, MK=7202076432en_US
dc.identifier.scopusauthoridSze, KN=7006735077en_US
dc.identifier.scopusauthoridYau, AC=7003439939en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats