Contrast enhancement of images using partitioned iterated function systems

dc.contributor.author	Οικονομόπουλος, Θεόδωρος Λ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-09T10:10:21Z
dc.date.available	2015-02-09T10:10:21Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5905
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com	en
dc.subject	Contrast enhancement
dc.subject	Iterated function system
dc.subject	Ενίσχυση αντίθεσης
dc.subject	Επαναλαμβανόμενο σύστημα λειτουργίας
dc.title	Contrast enhancement of images using partitioned iterated function systems	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.identifier.secondary	doi:10.1016/j.imavis.2009.04.011
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010
heal.bibliographicCitation	Economopoulos, T., Asvestas, P. and Matsopoulos, G. (January 2010). Contrast enhancement of images using partitioned iterated function systems. Image and Vision Computing. 28(1). pp. 45-54. Elsevier Science Ltd: 2010. Available from: http://www.sciencedirect.com [Accessed 05/05/2009]	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.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Image and Vision Computing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true