Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm

Authors
Jia Chen, Chuyi Li, Weiyu Yu
Corresponding Author
Jia Chen
Available Online October 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-312-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/ceie-16.2017.88How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - https://doi.org/10.2991/ceie-16.2017.88
ID  - Chen2016/10
ER  -