Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm

DOI

10.2991/ceie-16.2017.88How to use a DOI?

Keywords

Incomplete Beta Function; Image Enhancement; Artificial Bee Colony Algorithm

Abstract

In this paper, image enhancement is realized by using the Incomplete Beta Function (IBF) as the gray transformation curve. The main idea is to employ Artificial Bee Colony Algorithm (ABCA) to select the optimal parameters of IBF, which corresponds to the best curve of grayscale transformation. Designing specific fitness function constrains the evolutionary direction of the bees and then better images can be obtained. By comparing among the results of histogram equalization, unsharp masking, and Genetic Algorithm based methods, we come to the conclusion that ABCA is an effective method in image enhancement which is superior to the other three methods, and not only has the better optimizing ability than Genetic algorithm but also it converges quickly.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jia Chen
AU  - Chuyi Li
AU  - Weiyu Yu
PY  - 2016/10
DA  - 2016/10
TI  - Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 689
EP  - 695
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.88
DO  - 10.2991/ceie-16.2017.88
ID  - Chen2016/10
ER  -