Contrast enhancement of images using Partitioned Iterated Function Systems

Economopoulos, TL; Asvestas, PA; Matsopoulos, GK

dc.contributor.author	Economopoulos, TL	en
dc.contributor.author	Asvestas, PA	en
dc.contributor.author	Matsopoulos, GK	en
dc.date.accessioned	2014-03-01T01:33:03Z
dc.date.available	2014-03-01T01:33:03Z
dc.date.issued	2010	en
dc.identifier.issn	0262-8856	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20297
dc.subject	Contrast enhancement	en
dc.subject	Contrast Limited Adaptive Histogram Equalization	en
dc.subject	Iterated Function System	en
dc.subject	Linear and nonlinear unsharp masking	en
dc.subject	Local Range Modification	en
dc.subject	Self-similarity	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Optics	en
dc.subject.other	Contrast enhancement	en
dc.subject.other	Contrast Limited Adaptive Histogram Equalization	en
dc.subject.other	Iterated Function System	en
dc.subject.other	Linear and nonlinear unsharp masking	en
dc.subject.other	Local Range Modification	en
dc.subject.other	Self-similarity	en
dc.subject.other	Algorithms	en
dc.subject.other	Mathematical transformations	en
dc.title	Contrast enhancement of images using Partitioned Iterated Function Systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.imavis.2009.04.011	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.imavis.2009.04.011	en
heal.language	English	en
heal.publicationDate	2010	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 transformation of the gray levels is determined by two parameters which adjust the brightness and the contrast of the transformed block. The PIFS is used in order to create a lowpass 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. The proposed algorithm uses a predefined constant value for the contrast parameter, whereas, the parameters of the affine spatial transform, as well as the parameter adjusting the brightness, are calculated using k-dimensional trees. The lowpass version of the original image is obtained applying the PIFS on the original image repeatedly while using a value for the contrast parameter that is lower than the predefined one. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against four other widely used contrast enhancement methods; namely, linear and nonlinear unsharp masking, Contrast Limited Adaptive Histogram Equalization and Local Range Modification. (C) 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Image and Vision Computing	en
dc.identifier.doi	10.1016/j.imavis.2009.04.011	en
dc.identifier.isi	ISI:000272895000006	en
dc.identifier.volume	28	en
dc.identifier.issue	1	en
dc.identifier.spage	45	en
dc.identifier.epage	54	en