Contrast enhancement of images using partitioned iterated function systems

Economopoulos, T; Asvestas, P; Matsopoulos, G

dc.contributor.author	Economopoulos, T	en
dc.contributor.author	Asvestas, P	en
dc.contributor.author	Matsopoulos, G	en
dc.date.accessioned	2014-03-01T02:44:32Z
dc.date.available	2014-03-01T02:44:32Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31867
dc.subject	Contrast Enhanced	en
dc.subject	Fixed Point	en
dc.subject	Iterated Function System	en
dc.subject	Linear Transformation	en
dc.subject.other	Contractive transformations	en
dc.subject.other	Partitioned Iterated Function System (PIFS)	en
dc.subject.other	Spatial transforms	en
dc.subject.other	Algorithms	en
dc.subject.other	Image quality	en
dc.subject.other	Iterative methods	en
dc.subject.other	Pixels	en
dc.subject.other	Image enhancement	en
dc.title	Contrast enhancement of images using partitioned iterated function systems	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-74607-2_45	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-74607-2_45	en
heal.publicationDate	2007	en
heal.abstract	A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The PIFS is used in order to create a low-pass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against two other widely used contrast enhancement methods. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-74607-2_45	en
dc.identifier.volume	4678 LNCS	en
dc.identifier.spage	497	en
dc.identifier.epage	508	en