Abstract
Although image defogging is widely used in many working systems, existing defogging methods have some limitations due to the lack of enough information to solve the equation of fog formation model. To overcome the limitations, a novel defogging parameter selection algorithm based on artificial fish swarm algorithm (AFSA) is proposed in this paper. Two representative defogging algorithms are used to test the effectiveness of the method. The proposed method first selects the two main parameters and then optimizes them using the AFS algorithm. An assessment index of image defogging effect is used as the food concentration of the AFSA. Thus, these parameters may be adaptively and automatically adjusted for the defogging algorithms. A comparative study and qualitative evaluation demonstrate that better quality results are obtained by using the proposed method.
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References
Tan R.T.: Visibility in bad weather from a single image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE Computer Society, Anchorage (2008)
Nishino, K., Kratz, L., Lombardi, S.: Bayesian defogging. Int. J. Comput. Vision 98(3), 263–278 (2012)
He, K.M., Sun, J., Tang, X.O.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2201–2208. IEEE Computer Society, Kyoto (2009)
Lagorio, A., Grosso, E., Tistarelli, M.: Automatic detection of adverse weather conditions in traffic scenes. In: Proceedings of IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 273–279. IEEE Computer Society, Santa Fe (2008)
Hautiere, N., Tarel, J.-P., Aubert, D.: Towards fog-free in-vehicle vision systems through contrast restoration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2374–2381. IEEE Computer Society, Minneapolis (2007)
Hautiere, N., Tarel, J.-P., Halmaoui, H., Bremond, R., Aubert, D.: Enhanced fog detection and free-space segmentation for car navigation. Mach. Vis. Appl. 25(3), 667–679 (2014)
Li, L.X., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animate: fish swarm algorithm. In: Proceedings of system engineering theory and practice, pp. 32–38. IEEE Computer Society, Los Alamitos (2002)
Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4), 965–997 (2014)
Ye, Z.W., Li, Q.Y., Zeng, M.D., Liu, W.: Image segmentation using thresholding and artificial fish-swarm algorithm. In: Proceedings of International Conference on Computer Science and Service System, pp. 1529–1532. IEEE Computer Society, Los Alamitos (2012)
Janaki, S.D., Geetha, K.: Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm. Pol. J. Med. Phys. Eng. 23(2), 29–36 (2017)
Sui, D., He, F.: Image restoration algorithm based on artificial fish swarm micro decomposition of unknown priori pixel. Telkomnika 14(1), 187–194 (2016)
Zhu, J.L., Wang, Z.L., Liu, H.: Gray-scale image matching technology based on artificial fish swarm algorithm. Appl. Mech. Mater. 411–414, 1295–1298 (2013)
El-said, S.A.: Image quantization using improved artificial fish swarm algorithm. Soft. Comput. 19(9), 2667–2679 (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Ji, Z.X., Chen, Q., Sun, Q.S., Xia, D.D.: A moment-based nonlocal-means algorithm for image denoising. Inf. Process. Lett. 109(23–24), 1238–1244 (2009)
Guo, F., Tang, J., Cai, Z.X.: Objective measurement for image defogging algorithms. J. Cent. South Univ. 21(1), 272–286 (2014)
Huang, K.Q., Wang, Q., Wu, Z.Y.: Natural color image enhancement and evaluation algorithm based on human visual system. Comput. Vis. Image Underst. 103(1), 52–63 (2006)
Tarel, J.-P., Hautiere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)
He, K.M.: Single image haze removal using dark channel prior. Ph.D. dissertation, The Chinese University of Hong Kong (2011)
Acknowledgements
The authors would like to thank Erik Matlin and Kaelan Yee for providing He’s source code, and Dr. Tarel and Dr. Hautiere for providing the Matlab code of their approach. This work was supported by the National Natural Science Foundation of China (61502537), Hunan Provincial Natural Science Foundation of China (2018JJ3681), and the National Undergraduate Programs for Innovation.
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Guo, F., Lan, G., Xiao, X., Zou, B. (2018). Parameter Selection of Image Fog Removal Using Artificial Fish Swarm Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_4
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