Greedy Algorithm for Local Contrast Enhancement of Images

Kartic Subr, Aditi Majumder, Sandy Irani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present a technique that achieves local contrast enhancement by representing it as an optimization problem. For this, we first introduce a scalar objective function that estimates the average local contrast of the image; to achieve the contrast enhancement, we seek to maximize this objective function subject to strict constraints on the local gradients and the color range of the image. The former constraint controls the amount of contrast enhancement achieved while the latter prevents over or under saturation of the colors as a result of the enhancement. We propose a greedy iterative algorithm, controlled by a single parameter, to solve this optimization problem. Thus, our contrast enhancement is achieved without explicitly segmenting the image either in the spatial (multi-scale) or frequency (multi-resolution) domain. We demonstrate our method on both gray and color images and compare it with other existing global and local contrast enhancement techniques.
Original languageEnglish
Title of host publicationImage Analysis and Processing
Subtitle of host publicationICIAP 2005: 13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings
EditorsFabio Roli, Sergio Vitulano
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages9
ISBN (Print)978-3-540-31866-8
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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