Abstract
Images which are captured using camera can cause degradation in images by the effect of climatic conditions such as haze and fog. Image restoration makes a notable change in performing different application of computer vision and pattern recognition. The main aim of this paper is to improve the effect of fog and hazy images compared to the existing methods. The enhanced defogging system [EDS] consists of different image improvement techniques with a Dark Channel Prior [DCP] Algorithm to estimate the amount of fog is there in the images and transmission as well. Fusion based fog removal will reduce the amount of haze remained in those images. Experiments were done more than 100 images and the results are discussed below.
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References
Xu, Y., Wen, J., Fei, L., Zhang, Z.: Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4, 165–188 (2016)
Latha, M., Poojith, A., Reddy, B.V.A., Kumar, G.V.: Image processing in agriculture. Int. J. Innovative Res. Electr. Electron. Instrum. Control Eng. 2(6) (2014)
Tripathi, K., Mukhopadhyay, S.: Removal of fog from images: a review. IETE Tech. Rev. 29(2), 148–156 (2012)
Yu, X., Xiao, C., Deng, M., Peng, L.: A classification algorithm to distinguish image as haze or non-haze. In: Proceeding IEEE International Conference Image Graph. pp. 286–289 (2011)
Fang, S., Zhan, J., Cao, Y., Rao, R.: Improved single image de-hazing using segmentation. In: IEEE International Conference on Image Processing (ICIP), pp. 3589–92 (2010)
Tarel, J.P., Hautiere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transport. Syst. Mag. 4(2), 6–10 (2010)
Ding, M., Ruo, F.T.: Efficient dark channel based image dehazing using quadtrees. Sci. China Inf. Sci. 56(9), 1–9 (2013)
Xu, Z., Liu, X., Ji, N.: Fog removal from color images using contrast limited adaptive histogram equalization. In: Image and Signal Processing, 2009. CISP’09. 2nd International Congress on. IEEE (2009)
Vasudevan, S.K, Venkatachalam, K., Anandaram, S., Menon, A.J.: A novel method for circuit recognition through image processing techniques. Asian J. Inf. Tech. 15, 1146–1150 (2016)
Nayar; S.K., Narasimhan, S.G.: Vision in bad weather. In: IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 820–827 (1999)
Mishra, S., Sharma, T.: Image restoration technique for fog degraded image. Int. J. Comput. Trends Tech. 18(5), 208–213 (2014)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceeding IEEE Conference Computer Vision Pattern Recognition, pp. 1956–1963 (2009)
Jiang, J., Hou, T., Qi, M.: Improved algorithm on image haze removal using dark channel prior. Chinese J. Circuit Syst. 16(2), 7–12 (2011)
Sharma, R., Chopra, V.: A review on different image dehazing methods. Int. J. Comput. Eng. Appl. 6(3), 77–87 (2014)
Narasimhan, S.G., Nayar, S.K: Removing weather effects from monochrome images. In: Proceeding of the IEEE Computer Society Conference, Computer Vision Pattern Recognition (CVPR), vol. 2, pp. II-186–II-193 (2001)
Chen, Z., Shen, J., Roth, P.: Single image defogging algorithm based on dark channel priority. J. Multimedia 8(4) (2013)
Lan, X., Zhang, L., Shen, H., Yuan, Q., Li, H.: Single image haze removal considering sensor blur and noise. EURASIP J. Adv. Signal Process. 2013(1), 86 (2013)
Sabarish, B.A., Mohan, S.B, MamthaShri, D.P.B., Ajit, R.C.B., Arun, A.V.R.B.: Automating runout decisions in cricket using image processing. Int. J. Appl. Eng. Res. 10, 25493–25500 (2015)
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Krishnan, S., Sabarish, B.A., Gayathri, V., Padmavathi, S. (2018). Enhanced Defogging System on Foggy Digital Color Images. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_41
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DOI: https://doi.org/10.1007/978-3-319-71767-8_41
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