We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

by Sonali Talwar, Rajesh Kochher
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 3
Year of Publication: 2015
Authors: Sonali Talwar, Rajesh Kochher
10.5120/21045-3676

Sonali Talwar, Rajesh Kochher . Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal. International Journal of Computer Applications. 119, 3 ( June 2015), 9-14. DOI=10.5120/21045-3676

@article{ 10.5120/21045-3676,
author = { Sonali Talwar, Rajesh Kochher },
title = { Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 3 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number3/21045-3676/ },
doi = { 10.5120/21045-3676 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:01.781404+05:30
%A Sonali Talwar
%A Rajesh Kochher
%T Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 3
%P 9-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Underwater images are used to explore the unique life and the world that exists under the water. These images have less clarity, diminishing colors, low contrast . All these issues are a result of haze in underwater images. So, Underwater image haze removal algorithms become very important and necessary for many vision applications. In this paper, after a brief overview of existing methods of haze removal and discovering their limitations, we present a novel technique of underwater image haze removal based on Fuzzy Based DCP and AHE. AHE has been used to remove the problem of uneven illumination. The proposed technique has the ability to remove the limitations of existing techniques. Different kind of image quality assessment metrics have been used to evaluate the effectiveness of proposed technique over the existing one. The proposed technique give efficient results for different hazy underwater images by removing the haze to a good extent and improving the quality.

References
  1. S. M. Pizer et al. "Adaptive histogram equalization and its variations" Computer vision, graphics, and image processing (Elsevier), 39, no. 3, pp. 355-368, 1987.
  2. J. S Roger Jang and N. Gulley. "Fuzzy logic toolbox user's guide. " The Mathworks Inc1, 1995.
  3. H . D Cheng, and H. Xu "A novel fuzzy logic approach to contrast enhancement. " Pattern Recognition (Elsevier), 33, no. 5, pp. 809-819, 2000.
  4. J. A. Stark "Adaptive image contrast enhancement using generalizations of histogram equalization. " IEEE Transactions on Image Processing, 9, no. 5, pp. 889-896, 2000.
  5. J. Yuh and M. West "Underwater robotics. " Advanced Robotics, 15, no. 5, pp. 609-639, 2001.
  6. M. Hanmandlu, D. Jha, and R. Sharma. "Color image enhancement by fuzzy intensification" Pattern Recognition Letters (Elsevier), 24, no. 1, pp. 81-87, 2003.
  7. Y. Y. Schechner, N. Karpel "Recovery of underwater visibility and structure by polarization analysis", IEEE J. ournal of Oceanic Engineering, 30, no. 3, pp. 570–587, 2005.
  8. J. Heidemann , W. Ye, J. Wills,A. Syed & Y. Li "Research challenges and applications for underwater sensor networking" In Wireless Communications and Networking Conference, IEEE, Vol. 1, pp. 228-235, 2006.
  9. R. Fattal "Single image dehazing. " In ACM Transactions on Graphics (TOG), vol. 27, no. 3, 2008.
  10. N. Desai et al. "A Fuzzy Logic Based Approach to De-Weather Fog-Degraded Images. " In IEEE Sixth International Conference on Computer Graphics, Imaging and Visualization, pp. 383-387, 2009.
  11. R. Schettini and S. Corchs. "Underwater image processing: state of the art of restoration and image enhancement methods. " EURASIP Journal on Advances in Signal Processing, 2010.
  12. K. He, J. Sun, and X. Tang. "Single image haze removal using dark channel prior" IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, no. 12, pp. 2341-2353, 2011.
  13. H. Y. Yang, P. Y. Chen, C. C. Huang, Y. Z. Zhuang & H. Y. Shiau "Low complexity underwater image enhancement based on dark channel prior. " IEEE Second International Conference on Innovations in Bio-inspired Computing and Applications, pp. 17-20, 2011.
  14. A. T. Çelebi & S. Ertürk,"Visual enhancement of underwater images using Empirical Mode Decomposition" Expert Systems with Applications(Elsevier), 39(1), pp. 800-805, 2012.
  15. J. Y. Chiang and Y. C Chen. "Underwater image enhancement by wavelength compensation and dehazing. " IEEE Transactions on Image Processing, 21, no. 4, pp. 1756-1769, 2012.
  16. E. Ullah, R. Nawaz and J. Iqbal. "Single image haze removal using improved dark channel prior. " In Proceedings of IEEE International Conference on Modelling, Identification & Control, pp. 245-248, 2013.
  17. K. He, J. Sun, and X. Tang, "Guided image filtering", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, June 2013.
  18. Q. Zhu, S. Yang, P. A. Heng & X. Li "An adaptive and effective single image dehazing algorithm based on dark channel prior. " In IEEE International Conference on Robotics and Biomimetics,pp. 1796-1800, 2013.
  19. Y. J. Cheng et al. "Visibility Enhancement of Single Hazy Images Using Hybrid Dark Channel Prior. " In IEEE International Conference on Systems, Man, and Cybernetics, pp. 3627-3632, 2013.
  20. Z. Chen, J. Shen and P. Roth. "Single Image Defogging Algorithm based on Dark Channel Priority. " Journal of Multimedia, 8, no. 4, pp. 432-438, 2013.
  21. A. Alajarmeh, R. A. Salam,M. F. Marhusin & K. Abdulrahim "Real-time video enhancement for various weather conditions using dark channel and fuzzy logic. " In IEEE International Conference on Computer and Information Sciences, pp. 1-6, 2014.
  22. G. Raju and M. S. Nair. "A fast and efficient color image enhancement method based on fuzzy-logic and histogram. " AEU-International Journal of Electronics and Communications (Elsevier), 68, no. 3, pp. 237-243, 2014.
  23. P. F. Chen, J. K. Guo, C. C. Sung & H. H. Chang "An Improved Dark Channel-Based Algorithm for Underwater Image Restoration. " In Advanced Materials Springer International Publishing, pp. 311-316, 2014.
  24. S. Serikawa and H. Lu "Underwater image dehazing using joint trilateral filter. " Computers & Electrical Engineering (Elsevier), 40, no. 1, pp. 41-50, 2014.
  25. "Adaptive Histogram Equalization", [Online Available], http://www. wikipedia. com
  26. "Mean Squared Error", [Online Available], http://www. wikipedia. com
  27. "Peak signal to noise Ratio", [Online Available], http://www. wikipedia. com
  28. "Root mean square deviation", [Online Available], http://www. wikipedia. com
Index Terms

Computer Science
Information Sciences

Keywords

Underwater Image Haze Removal Fuzzy Logic

Powered by PhDFocusTM