CFP last date
20 January 2025
Reseach Article

Image Dehazing of Images Captured in Real-World Weather Conditions using Derived Guided Filter

by Mayuri Madake, Sankirti Shiravale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 3
Year of Publication: 2018
Authors: Mayuri Madake, Sankirti Shiravale
10.5120/ijca2018917494

Mayuri Madake, Sankirti Shiravale . Image Dehazing of Images Captured in Real-World Weather Conditions using Derived Guided Filter. International Journal of Computer Applications. 182, 3 ( Jul 2018), 40-45. DOI=10.5120/ijca2018917494

@article{ 10.5120/ijca2018917494,
author = { Mayuri Madake, Sankirti Shiravale },
title = { Image Dehazing of Images Captured in Real-World Weather Conditions using Derived Guided Filter },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 3 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number3/29746-2018917494/ },
doi = { 10.5120/ijca2018917494 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:19.220441+05:30
%A Mayuri Madake
%A Sankirti Shiravale
%T Image Dehazing of Images Captured in Real-World Weather Conditions using Derived Guided Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 3
%P 40-45
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Haze formation is the combination of airlight and attenuation. Attenuation decreases the contrast and airlight increases the whiteness in the scene. Atmospheric conditions created by floating particles such as fog and haze, severally degrade image quality. Removing haze from single image of a weather degraded scene found to be a difficult task because the haze is dependent on the unknown depth information, haze removal algorithms become more beneficial for many vision applications such as surveillance system, object detection, tracking and segmentation. This work focuses on removing block artifacts and degradation factors from a natural scene image containing fog, haze such that the enhancement of that image becomes very easy. In this paper Derived Guided Filter based visibility restoration approach will be used in order to solve the inadequate estimation of transmission map and color cast problem. The Dark Channel Prior along with derived Guided Filter is selected with an aim to apply techniques such as denoising, color correction, and implementing other forms of enhancement in a single image dehazing system.

References
  1. Wencheng Wang et.al, “Fast Image Dehazing Methods using Linear Transformation”,  IEEE Transactions on Multimedia ( Volume: 19, Issue: 6, June 2017 )
  2. Shih-Chia Huang et.al. “An Advanced Single-Image Visibility Restoration Algorithm for Real –World Hazy Scenes”, IEEE Transactions on Industrial Electronics, Vol. 62, No5, May2015.
  3. Shih-Chia Huang et.al., “Visibility restoration of single hazy images captured in real-world weather conditions”, IEEE Transactions on circuits and systems for video technology, Vol 24, No. 10, Year 2014.
  4. Kaiming He et.al., “Guided Image Filtering” IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 35, No. X, xxxxxxx 2013.
  5. Kaiming He et.al. “Single Image Haze Removal Using Dark Channel Prior”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 38, No. 12. Year 2011.
  6. Ramandeep Kaur, Nitika Kapoor, “A Review on enhancement on foggy images”, International Journal of Advances in Science and Technology (IJAST), Year: 2011
  7. E. Namer, S. Shwartz,Y.Y. Schechner, “Skylesspolarimetric calibration and visibility enhancement”, Optics Expres., vol. 17, no. 2, pp. 472- 493,2009.
  8. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep Photo: Model-Based Photograph Enhancement and Viewing”, ACM Trans. Graphics., Vol. 27, No. 5, pp. 116:1-116:10, Dec. 2008.
  9. R. Tan, “Visibility in Bad Weather from a Single Image”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Jun. 2008.
  10. R. Fattal, “Single Image Dehazing”, Proc. ACM SIGGRAPH08,2008.
  11. S.G. Narasimhan, and S.K. Nayar, “Interactive (De)Weathering of an Image using Physical Models, ICCV Workshop on Color and Photometric Methods in Computer Vision., pp. 1387-1394 Oct, 2003.
  12. Y.Y. Schechner, S.G. Narasimhan, and S.K. Nayar, Polarization Based Vision Through Haze, Proc. Applied Optics, special issue: light and color in the open air, vol. 42, no. no. 3, Jan. 2003.
  13. 13] S.G. Narasimhan, and S.K. Nayar, “Contrast Restoration of Weather Degraded Images”, IEEE Trans. Pattern Anal. Machine Intell., vol. 25, no. 6, pp. 713-724, Jun. 2003.
  14. S.G. Narasimhan and S.K. Nayar, “Removing Weather Effects from Monochrome Images”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
  15. K. Tan, and J. P. Oakley, “Enhancement of Color Images in Poor Visibility Conditions, Proc. IEEE Int. Conf. Image. Proc. (ICIP2000), Vol. 2, pp. 788-791, Sep. 2000.
  16. S.K. Nayar, and S.G. Narasimhan, “Vision in Bad Weather”, Proc.Seventh IEEE Intl Conf. Computer Vision, Vol. 2, pp. 820-827, Jun. 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Dark Channel Prior Derived Guided Filter Dehazing Fog Haze Transmission Map