Intensity-preserving contrast enhancement for gray-level images using multi-objective particle swarm optimization

被引：11

作者：

Kwok, N. M. ^{[1
]}

Ha, Q. P. ^{[1
]}

Liu, D. K. ^{[1
]}

Fang, G. ^{[2
]}

机构：

[1] Univ Technol Sydney, Fac Engn, ARC Ctr Excellence Autonomous Syst CAS, Broadway, NSW 2007, Australia

[2] Univ Western Sydney, Sch Engn, Penrith, NSW 1797, Australia

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, VOLS 1 AND 2 | 2006年

基金：

澳大利亚研究理事会;

关键词：

intensity preservation; contrast enhancement; multi-objective optimization; particle swarm;

D O I：

10.1109/COASE.2006.326849

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses the enhancement of the contrast of gray-level digital images while preserving the mean image intensity, thus, providing better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image with a continuous intensity transform function and the mean image intensity is preserved, by using the gamma-correction approach. Since the contrast enhancement and intensity preservation are contradicting, a multi-objective particle swarm optimization (MPSO) algorithm is developed to resolve this trade-off. Benchmark images, street senses and skyline images are included to illustrate the effectiveness of the proposed approach.

引用

页码：21 / +

页数：3

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