Abstract
Due to the lack of enough information to solve the equation of image degradation model, existing defogging methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting leads to difficulty in obtaining the best defogging results for different input foggy images. Therefore, a single image defogging algorithm based on particle swarm optimization (PSO) is proposed in this letter to adaptively and automatically select optimal parameter values for image defogging algorithms. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the PSO algorithm. Comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed parameter selection method.
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References
Tan R.T., Visibility in Bad Weather from a Single Image, IEEE Conference on Computer Vision and Pattern Recognition, 1 (2008).
Nishino K., Kratz L. and Lombardi S., International Journal of Computer Vision 98, 263 (2012).
He K.M., Sun J. and Tang X.O., IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 2341 (2011).
Tarel J.P. and Hautiere N., Fast Visibility Restoration from a Single Color or Gray Level Image, IEEE International Conference on Computer Vision, 2201 (2009).
Lagorio A., Grosso E. and Tistarelli M., Automatic Detection of Adverse Weather Conditions in Traffic Scenes, IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 273 (2008).
Hautiere N., Tarel J.-P. and Aubert D., Towards Fog-Free in-Vehicle Vision Systems through Contrast Restoration, IEEE Conference on Computer Vision and Pattern Recognition, 2374 (2007).
Hautiere N., Tarel J.-P., Halmaoui H., Bremond R. and Aubert D., Machine Vision and Applications 25, 667 (2014).
Jiji G.W. and DuraiRaj P.J., Applied Soft Computing 30, 650 (2015).
Li Y.Y., Jiao L.C., Shang R.H. and Stolkin R, Information Sciences 294, 408 (2015).
Wang Z., Bovik A.C., Sheikh H.R. and Simoncelli E.P., IEEE Trans. Image Processing 13, 600 (2004).
Ji Z.X., Chen Q., Sun Q.S. and Xia D.D., Information Processing Letters 109, 1238 (2009).
He K.M., Single Image Haze Removal using Dark Channel Prior, Ph.D. dissertation, The Chinese University of Hong Kong, 2011.
Guo F., Tang J. and Cai Z.X., J. Cent. South Univ. 21, 272 (2014).
Kennedy J. and Eberhart R.C., Particle Swarm Optimization, International Conference Neural Network, 1942 (1995).
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This work has been supported by the National Natural Science Foundation of China (Nos.61573380 and 61502537), and the Postdoctoral Science Foundation of Central South University. This paper was presented in part at the CCF Chinese Conference on Computer Vision, Tianjin, 2017. This paper was recommended by the program committee.
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Guo, F., Zhou, C., Liu, Lj. et al. Single image defogging based on particle swarm optimization. Optoelectron. Lett. 13, 452–456 (2017). https://doi.org/10.1007/s11801-017-7189-0
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DOI: https://doi.org/10.1007/s11801-017-7189-0