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Classical and Evolutionary Image Contrast Enhancement Techniques: Comparison by Case Studies

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 556)

Abstract

Histogram equalization (HE) and histogram stretching (HS) are two commonly used classical approaches for improving the appearance of a poor image. Such approaches may end up at developing artefacts, rendering the image unusable. Moreover these two classical approaches involve algorithmically complex tasks. On the other hand evolutionary soft computing methods claim to offer hassle free and effective contrast enhancement. In the present work, we report development of algorithms for two evolutionary approaches viz. genetic algorithm (GA) and artificial bee colony (ABC) and went on to evaluate the contrast enhancement capability of these algorithms using some test images. Further we compared the output images obtained using above two evolutionary approaches with the output images got using HE and HS. We report that evolutionary methods result in better contrast enhancement than classical methods in all our test cases. ABC approach outperformed GA approach, when output images were subjected to quantitative comparison.

Keywords

Image contrast enhancement Histogram equalization (HE) Histogram stretching (HS) Genetic algorithm (GA) Artificial bee colony (ABC) algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationCollege of Engineering & Technology (CET)BhubaneswarIndia

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