Abstract
A fast approach is proposed for image dehazing using fuzzy system and hybrid evolutionary algorithm through fuzzy contrast enhancement. First, the RGB color space is converted into HSV color space and Gaussian membership function (MF) is used for the fuzzification. Then a parametric sigmoid function is used for the haze image contrast enhancement. Finally, an objective function combining with the entropy and the visual factors is optimal using a hybrid evolutionary algorithm (HEA). HEA is presented based on Partial Swarm Optimization (PSO) algorithm and Genetic algorithm (GA). On comparison, this approach is found applicable for image dehazing and better than the artificial ant colony system (AACS)-based method [1].
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61079001), China’s 863 Program (No. 2011AA110301), China’s PH.D. Program Foundation (No. 20111103110017).
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Song, H., Gao, Y., Chen, Y. (2014). Fast Image Dehazing Using Fuzzy System and Hybrid Evolutionary Algorithm. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_25
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DOI: https://doi.org/10.1007/978-3-642-37835-5_25
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