Piecewise Wiener filter model based on fuzzy partition of local wavelet features for image restoration

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dc.contributor.author Stephanakis Ioannis, M en
dc.contributor.author Stamou, George en
dc.contributor.author Kollias, Stefanos en
dc.date.accessioned 2014-03-01T02:41:37Z
dc.date.available 2014-03-01T02:41:37Z
dc.date.issued 1999 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30565
dc.subject Fuzzy C Means Algorithm en
dc.subject Image Filtering en
dc.subject Image Restoration en
dc.subject Linear Optimization en
dc.subject Signal To Noise Ratio en
dc.subject Unsupervised Clustering en
dc.subject Wiener Filter en
dc.subject.other Fuzzy sets en
dc.subject.other Image enhancement en
dc.subject.other Mathematical models en
dc.subject.other Optimization en
dc.subject.other Signal filtering and prediction en
dc.subject.other Signal to noise ratio en
dc.subject.other Local wavelet features en
dc.subject.other Piecewise Wiener filter en
dc.subject.other Image reconstruction en
dc.title Piecewise Wiener filter model based on fuzzy partition of local wavelet features for image restoration en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IJCNN.1999.833503 en
heal.identifier.secondary http://dx.doi.org/10.1109/IJCNN.1999.833503 en
heal.publicationDate 1999 en
heal.abstract Autoregressive Wiener filters are used for prediction and restoration of still frame and video images. Nevertheless filters of this kind solve a linear optimization problem for the global statistics of an image. They fail when image statistics vary in space (non-stationarity) and when the corrupting noise is non-linear. A piecewise Wiener filter defined upon a fuzzy partition of the space of local wavelet features is presented and successfully applied to image restoration in the aforementioned cases. Unsupervised clustering of the features using the Bezdek fuzzy c-means algorithm is performed for region estimation and subsequent application of the proper filter hR(k) (n,m) according to a degree of belief μR(k). Experimental results indicate higher improvements in Signal-to-Noise-Ratios of corrupted images using the proposed method. en
heal.publisher IEEE, United States en
heal.journalName Proceedings of the International Joint Conference on Neural Networks en
dc.identifier.doi 10.1109/IJCNN.1999.833503 en
dc.identifier.volume 4 en
dc.identifier.spage 2690 en
dc.identifier.epage 2693 en

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