Affiliations Dept. of Computer Science, Zakho Technical Institute, Duhok Polytechnic University, Zakho, Iraq
Nowadays the role of computer vision and graphic have seen in wide application fields, so haze and fog fetch trouble to many computer vision and often effect on graphics applications as it diminishes the scene’s clarity. Haze forms when climate conditions stay slack for a time-frame. Building on the bearing of view as for the sun it might be brownish or bluish. Haze reduces the contrast and saturation degraded the quality of preview and captured the image. So it attenuates the mild pondered from the scenes and similarly blends it with some additive light inside the atmosphere. Here comes the role of the dehazing method though is very important in computer vision applications, it can take off haze from the pictures, increment the scene vision. From earlier up to now there are many methods have been proposed for improving images, single image dehazing method is one of them, and recently the researchers are more interesting with this method. The goal of this study firstly gives a brief introduction to image enhancement and restoration algorithms and suggested a variety of dehazing algorithm. Secondly, explore the different techniques of single image dehazing to remove the haze professionally from the digital images. Finally, summarized the comparison among these methods based on image quality assessment.
Falah Ibrahim.MSM Rahim (2018). Current Issues on Single Image Dehazing Method. International Journal of Computer Engineering In Research Trends, 5(2), 37-49. Retrieved from http://ijcert.org/ems/ijcert_papers/V5I205.pdf
Keywords : Dehazing Method, Single image, Outdoor image, Image restoration, Image Enhancement, Dark Channel.
1. Sharma R, Chopra V. A review of different image dehazing methods.
2. Patel SP, Nakrani M. A Review on Methods of Image Dehazing.
3. Ding M, Wei L. Single-image haze removal using the mean vector L2-norm of RGB image sample window. Opt-Int J Light Electron Opt. 2015;126(23):3522–8.
4. Lee S, Yun S, Nam J-H, Won CS, Jung S-W. A review of dark channel prior based image dehazing algorithms. EURASIP J Image Video Process. 2016;2016(1):4.
5. Lu H, Li Y, Nakashima S, Serikawa S. Single image dehazing through improved atmospheric light estimation. Multimedia Tools Appl. 2016;75(24):17081–96.
6. Chengtao C, Qiuyu Z, Yanhua L. A survey of image dehazing approaches. In IEEE; 2015. p. 3964–9.
7. Liang J, Ren L, Ju H, Zhang W, Qu E. Polarimetric dehazing method for dense haze removal based on distribution analysis of angle of polarization. Opt Express. 2015;23(20):26146–57.
8. Kaufman Y, Tanré D, Gordon H, Nakajima T, Lenoble J, Frouin R, et al. Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. J Geophys Res-Atmospheres. 1997;102(D14):16815–30.
9. Singh D, Kumar V. Comprehensive survey on haze removal techniques. Multimedia Tools Appl. 2017;1–26.
10. Badhe MV, Prabhakar LR. A Survey on Haze removal using Image visibility Restoration Technique. Int J Comput Sci Mob Comput. 2016;5(2):96–101.
11. Minnaert M, Singer S. The Nature of Light and Colour in the Open Air. Phys Today. 1954;7:16.
12. Unsworth MH. Daylight and its spectrum (second edition). By S. T. Henderson. Adam Hilger Ltd. (Bristol), 1977. Pp. 349 + x, 87 figs., 8 plates, 9 tables. Q J R Meteorol Soc. 1979 Jan 1;105(443):320–320.
13. McCartney EJ. Optics of the atmosphere: scattering by molecules and particles. N Y John Wiley Sons Inc 1976 421 P. 1976;
14. Narasimhan SG, Nayar SK. Vision and the atmosphere. Int J Comput Vis. 2002;48(3):233–54.
15. Narasimhan SG, Nayar SK. Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell. 2003;25(6):713–24.
16. Nayar SK, Narasimhan SG. Vision in bad weather. In IEEE; 1999. p. 820–7.
17. Long J, Shi Z, Tang W, Zhang C. Single remote sensing image dehazing. IEEE Geosci Remote Sens Lett. 2014;11(1):59–63.
18. Ge G, Wei Z, Zhao J. Fast single-image dehazing using linear transformation. Opt-Int J Light Electron Opt. 2015;126(21):3245–52.
19. Pan X, Xie F, Jiang Z, Yin J. Haze removal for a single remote sensing image based on deformed haze imaging model. IEEE Signal Process Lett. 2015;22(10):1806–10.
20. He K, Sun J, Tang X. X.: Single image haze removal using dark channel prior. 2009;
21. Alharbi EM, Ge P, Wang H. A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior. J Comput Commun. 2016;4(02):47.
22. Das D, Chaudhuri SS, Roy S. Dehazing technique based on the dark channel prior model with sky masking and its quantitative analysis. In IEEE; 2016. p. 207–10.
23. Kil TH, Lee SH, Cho NI. A dehazing algorithm using dark channel prior and contrast enhancement. In IEEE; 2013. p. 2484–7.
24. Ullah E, Nawaz R, Iqbal J. Single image haze removal using improved dark channel prior. In IEEE; 2013. p. 245–8.
25. Zhang T, Chen Y. Single image dehazing based on improved dark channel prior. In Springer; 2015. p. 205–12.
26. Chengtao C, Qiuyu Z, Yanhua L. Improved dark channel prior dehazing approach using adaptive factor. In IEEE; 2015. p. 1703–7.
27. Yu H, Cai C. An adaptive factor-based method for improving dark channel prior dehazing. In IEEE; 2016. p. 417–20.
28. Lu X, Lv G, Lei T. Single image dehazing based on multiple scattering model. In IEEE; 2014. p. 239–44.
29. Wang R, Li R, Sun H. Haze removal based on multiple scattering model with superpixel algorithm. Signal Process. 2016;127:24–36.
30. Wang W, Yuan X, Wu X, Liu Y, Ghanbarzadeh S. An efficient method for image dehazing. In IEEE; 2016. p. 2241–5.
31. Tan RT. Visibility in bad weather from a single image. In IEEE; 2008. p. 1–8.
32. Fattal R. Single image dehazing. ACM Trans Graph TOG. 2008;27(3):72.
33. Tarel J-P, Hautiere N. Fast visibility restoration from a single color or gray level image. In IEEE; 2009. p. 2201–8.
34. Fattal R. Dehazing using color-lines. ACM Trans Graph TOG. 2014;34(1):13.
35. Tang K, Yang J, Wang J. Investigating haze-relevant features in a learning framework for image dehazing. In 2014. p. 2995–3000.
36. Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process. 2015;24(11):3522–33.
37. Cai B, Xu X, Jia K, Qing C, Tao D. Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process. 2016;25(11):5187–98.
38. Berman D, Avidan S. Non-local image dehazing. In 2016. p. 1674–82.
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