Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1987
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSa, Pankaj K-
dc.contributor.authorMajhi, B-
dc.date.accessioned2013-09-19T10:55:14Z-
dc.date.available2013-09-19T10:55:14Z-
dc.date.issued2013-07-
dc.identifier.citationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), July 7–10, 2013, Hyderabad, India.en
dc.identifier.urihttp://hdl.handle.net/2080/1987-
dc.descriptionCopyright belongs to proceeding publisheren
dc.description.abstractThe point spread functions (PSF) responsible for degrading the observed images are very often not known. Hence, the image must be restored only from the available noisy blurred observation. This paper proposes two new image restoration algorithms, which are based on support vector regression (SVR). The first algorithm uses local variance and the second algorithm utilizes the concepts of fuzzy systems to counter blur in a given image. These algorithms significantly reduce the training time through their effective sample selection mechanisms. Experimental findings show that the proposed techniques deliver superior results for a variety of blurs and PSFs. Image restoration, point spread function (PSF), support vector regression (SVR), fuzzy systems.en
dc.format.extent851790 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.titleSupport Vector Regression based Image Restorationen
dc.typeArticleen
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
DSpace-Pankaj-SVR-FUZZIEEE (2).pdf831.83 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.