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Image Dehazing using PCA Fusion Technique for Enhanced Road Visibility

by Pranali U. Naik, Samarth Borkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 46
Year of Publication: 2018
Authors: Pranali U. Naik, Samarth Borkar
10.5120/ijca2018917204

Pranali U. Naik, Samarth Borkar . Image Dehazing using PCA Fusion Technique for Enhanced Road Visibility. International Journal of Computer Applications. 180, 46 ( Jun 2018), 10-15. DOI=10.5120/ijca2018917204

@article{ 10.5120/ijca2018917204,
author = { Pranali U. Naik, Samarth Borkar },
title = { Image Dehazing using PCA Fusion Technique for Enhanced Road Visibility },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 46 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number46/29544-2018917204/ },
doi = { 10.5120/ijca2018917204 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:43.858573+05:30
%A Pranali U. Naik
%A Samarth Borkar
%T Image Dehazing using PCA Fusion Technique for Enhanced Road Visibility
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 46
%P 10-15
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Presence of fog and haze significantly reduces the visibility of a scene. Better visibility is crucial for all computer vision applications thus recovering images impaired by haze or dehazing finds its application in the fields of surveillance, tracking, detection and restoration. In this paper fusion based approach using principle component analysis (PCA) technique has been adopted. The novelty of this algorithm is that it does not require any haze depth generation as normally required in many existing methods. Using the original image two images are derived on these images contrast adjustment, and contrast normalization techniques are performed. PCA fusion improves fused image quality and resolution. This method only requires the original image and is simple and easy to implement. As the haze impaired image appears whitish and blurry the details of the road become less evident thus making driving in foggy weather conditions unsafe. Thus, the proposed method concentrates on dehazing for better road visibility. The qualitative and quantitative comparison as compared with existing color fidelity and contrast reveals that our proposed novel method is better at restoring color fidelity and enhancing contrast.

References
  1. S. P. Patel and M. Nakrani, “A review on methods of image dehazing,” International Journal of Computer Applications (0975 – 8887) Volume 133 – No.12, January 2016.
  2. W.Wang and X.Yaun, “Recent advances in image dehazing”, IEEE/CAA Journal of Automatica Sinica, Vol-4, 2017.
  3. C. Chengtao, Z. Qiuyu and L. Yanhua, “A survey of image dehazing approaches” IEEE/CCDC, 23-25 May 2015.
  4. B.A. Baumann, M. Boltz, J. Ebling, M. Koenig, H.S. Loos, M. Merkel, W. Neim, J. K. Warzelhan and J. Yu “A review and comparison of measures for automatic video surveillance systems,” EURASIP Journal on Image and Video Processing 2008, 30 (2008).
  5. E Kermani, D Asemani, “A robust adaptive algorithm of moving object detection for video surveillance”, EURASIP J. Image Video Process. 2014(27), 1–9 (2014)
  6. D. Neelima and G. Mamidisetti, “A computer vision model for vehicle detection in traffic surveillance,” International Jjournal of Science and Advance Technology Volume-2, Issue-5, 1203 – 1209.
  7. C. O. Ancuti, C. Ancuti, and P. Bekaert, “Effective single image dehazing by fusion,” in Proc. 17th IEEE Int. Conf. Image Process., Hong Kong, China, 2010, pp. 3541-3544.
  8. Y. Yang, D.S. Park, S. H., and N. Rao, " Medical image fusion via an effective wavelet-based approach", EURASIP Journal on Advances in Signal Processing Volume 2010.
  9. K.Kim,“Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering”, in IEEE Trans.Consum.Electron, 1998.
  10. Z. Guo, “Objective assessment method for the clearness effect of image defogging algorithm”, Acta Automatica Sinica, 201239(9):1410-1419.
  11. Y.Y. Schechner, S.G. Narasimhan and S.K. Nayar, “Instant Dehazing Of Images Using Polarization”, Proc. Computer Vision & Pattern Recognition Vol. 1(2001).
  12. A. S. Narasimhan and S. Nayar, “Contrast restoration of weather degraded images”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 6, June 2003.
  13. K. Katiyar and N.Verma, “Single image haze removal algorithm using color attenuation prior and multi-Scale fusion”, International Journal of Computer Applications (0975 – 8887) Volume 141 – No.10, May 2016.
  14. R. T. Tan, “Visibility in bad weather from a single image” , in IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
  15. J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proc. IEEE Int. Conf. Comput. Vis., Sep.–Oct. 2009, pp. 2201–2208.
  16. R. Fattal, “Single image dehazing,” ACM Trans. Graph., vol. 27, no. 3, p. 72, Aug. 2008.
  17. L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image”, in Proc. IEEE Int. Conf. Comput. Vis., Sep.–Oct. 2009, pp. 1701–1708.
  18. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, December 2011.
  19. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: model-based photograph viewing,” ACM Trans. Graph., vol. 27, no. 5, p. 116, 2008.
  20. L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image”, in Proc. IEEE Int. Conf. Comput. Vis., Sep.–Oct. 2009, pp. 1701–1708.
  21. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior”, In IEEE CVPR, 2009.
  22. N. Hautiere, J.-P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges”, J. Image Anal. Stereo, vol. 27, pp. 87–95, 2008.
  23. K. Iqbal, M. Odetayo, A. James, R. A enhancement and. Salam and A. Z. Talib,“Enhancing the low quality images using unsupervised color correction methods”, in IEEE International Conference on Systems Man and Cybernetics, Istanbul, 2010.
  24. V. Gupta and A.V. Band, “Adaptive improved PCA with wavelet transform for image denoising”, IJCA (0975 – 8887) Volume 82,, November 2013.
  25. S. Pal and R.Mahakud , M. Sahoo, “PCA based Image Denoising using local pixel grouping”, IJCA Special Issue on “2nd National Conference- Computing, Communication and Sensor Network” CCSN, 2011.
  26. V.P.S. Naidu and J.R. Rao, “Pixel level fsion using pribciple component analysis” Defence Science Journal, Vol. 58, May 2008, pp. 338-352,2008.
  27. R.P. Desale and S. V. Verma “Study and analysis of PCA, DCT & DWT based image fusion techniques”ICSRP-7-8 Feb. 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Principle Component Analysis Contrast normalization Dehazing Haze depth

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