Abstract
Images captured in adverse weather condition critically degrade the quality of an image and thereby reduces the visibility of an image. This, in turn, affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring the clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this paper, a comparative analysis also has been made on different existing image defogging algorithms. And a defogging technique called Dark Channel Prior Technique on images has been implemented. We perform a in depth study of this technique and establish its pseudo code which is the contribution of the paper. Experimental results show that the used method shows efficient results by significantly improving the visual effects of the image in foggy weather but this method has some limitations too for the images containing sky region. We have also performed some objective measurement on the images to determine the technique used. Finally, we conclude the whole work with its relative advantages and shortcomings.
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References
Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820–827 (1999)
Al-Zubaidy, Y., Salam, R.A.: Removal of atmospheric particles in poor visibility outdoor images. Telkomnika 11(8), 4244–4250 (2013)
Oakley, J.P., Bu, H.: Correction of simple contrast loss in color images. IEEE Trans. Image Process. 16(2), 511 (2007)
Tan, R.: Visibility in bad weather from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2008
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Wang, J., He, N., Zhang, L., Lu, K.: Single image dehazing with a physical model and dark channel prior (2015)
Chen, J., Chau, L.: An enhanced window-variant dark channel prior for depth estimation using single foggy image. In: IEEE Conference (2014)
Xu, H., Guo, J., Liu, Q., Ye, L.: Fast image dehazing using improved dark channel prior. In: IEEE International Conference on Information Science and Technology, 23–25th March 2012 (2012)
Hautiere, N., Tarel, J.-P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient rationing at visible edges. J. Image Anal. Stereol. 27(2), 87–95 (2008)
Sakuldee, R., Udomhunsakul, S.: Objective performance of compressed image quality assessments. Int. J. Comput. Electr. Autom. Control Inf. Eng. 1(11) (2007)
Pal, T., Bhowmik, M.K., Ghosh, A.K.: Defogging of visual images using SAMEER-TU database. In: Proceedings of the Elsevier International Conference on Information and Communication Technologies, 3–5 December 2014 (2014)
Pal, T., Bhowmik, M.K., Ghosh, A.K.: Contrast restoration of fog-degraded image sequences. In: Das, K.N., Deep, K., Pant, M., Bansal, J.C., Nagar, A. (eds.) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. AISC, vol. 335, pp. 325–338. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2217-0_28
Pal, T., Bhowmik, M.K., Bhattacharjee, D., Ghosh, A.K.: Visibility enhancement techniques for fog degraded images: a comparative analysis with performance evaluation. In: 26th IEEE Conferencce on TENCON on Technologies for Smart Nation, Marina Bay Sands, Singapore (2016)
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Pal, T., Datta, A., Das, T., Das, I., Chakma, D. (2019). A Review on Image Defogging Techniques Based on Dark Channel Prior. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_25
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DOI: https://doi.org/10.1007/978-981-13-8578-0_25
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