Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm
Jia Chen, Chuyi Li, Weiyu Yu
Available Online October 2016.
- https://doi.org/10.2991/ceie-16.2017.88How to use a DOI?
- Incomplete Beta Function; Image Enhancement; Artificial Bee Colony Algorithm
- 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.
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 -