Modified Sigmoid Function Based Gray Scale Image Contrast Enhancement Using Particle Swarm Optimization

被引：9

作者：

Verma H.K. ^{[1
]}

Pal S. ^{[1
]}

机构：

[1] Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science, Indore, 452003, Madhya Pradesh

来源：

Journal of The Institution of Engineers (India): Series B | 2016年 / 97卷 / 02期

关键词：

Contrast; Contrast enhancement; Entropy; Modified sigmoid function (MSF); Particle swarm optimization (PSO);

D O I：

10.1007/s40031-014-0175-z

中图分类号：

学科分类号：

摘要：

The main objective of an image enhancement is to improve eminence by maximizing the information content in the test image. Conventional contrast enhancement techniques either often fails to produce reasonable results for a broad variety of low-contrast and high contrast images, or cannot be automatically applied to different images, because they are parameters dependent. Hence this paper introduces a novel hybrid image enhancement approach by taking both the local and global information of an image. In the present work, sigmoid function is being modified on the basis of contrast of the images. The gray image enhancement problem is treated as nonlinear optimization problem with several constraints and solved by particle swarm optimization. The entropy and edge information is included in the objective function as quality measure of an image. The effectiveness of modified sigmoid function based enhancement over conventional methods namely linear contrast stretching, histogram equalization, and adaptive histogram equalization are better revealed by the enhanced images and further validated by statistical analysis of these images. © 2014, The Institution of Engineers (India).

引用

页码：243 / 251

页数：8

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