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

Stephanakis Ioannis, M; Stamou, George; Kollias, Stefanos

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