A fast adaptive approach to the restoration of images degraded by noise

Boutalis, Y; Kollias, S; Carayannis, G

dc.contributor.author	Boutalis, Y	en
dc.contributor.author	Kollias, S	en
dc.contributor.author	Carayannis, G	en
dc.date.accessioned	2014-03-01T01:07:45Z
dc.date.available	2014-03-01T01:07:45Z
dc.date.issued	1990	en
dc.identifier.issn	0165-1684	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/10147
dc.subject	adaptive	en
dc.subject	estimation	en
dc.subject	image	en
dc.subject	Kalman filtering	en
dc.subject	Recursive	en
dc.subject	restoration	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Noise, Spurious Signal	en
dc.subject.other	Signal Filtering and Prediction--Kalman Filtering	en
dc.subject.other	Signal Interference--Suppression	en
dc.subject.other	2-D Kalman Filtering	en
dc.subject.other	Fast Adaptive Approach	en
dc.subject.other	Kalman Strip Filter	en
dc.subject.other	Multichannel Space-Recursive Estimation	en
dc.subject.other	Noisy Images	en
dc.subject.other	Reduced Update Kalman Filter	en
dc.subject.other	Image Processing	en
dc.title	A fast adaptive approach to the restoration of images degraded by noise	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0165-1684(90)90037-Y	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0165-1684(90)90037-Y	en
heal.language	English	en
heal.publicationDate	1990	en
heal.abstract	An adaptive approach to the restoration of noisy images is presented in this paper. Two recursive schemes are developed, which simultaneously estimate the unknown image model and restore the image. In the first, the reduced update Kalman filter (RUKF) is appropriately combined with a fast multichannel space-recursive estimation technique (FAMSRET). The computational load associated with the filter is significantly reduced in the second scheme. A low-order state-space image model is derived for this purpose. A Kalman filter, combined with the FAMSRET algorithm, is then applied to this model providing a fast adaptive technique for the suppression of noise in images. Examples are given, using real image data, which illustrate the performance of the proposed schemes. © 1990.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Signal Processing	en
dc.identifier.doi	10.1016/0165-1684(90)90037-Y	en
dc.identifier.isi	ISI:A1990CV09300005	en
dc.identifier.volume	19	en
dc.identifier.issue	2	en
dc.identifier.spage	151	en
dc.identifier.epage	162	en