CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Visibility Enhancement of Underwater Hazy Image using Multi-model SVD-DWT Fusion

by Aditi Mehrolia, Aditya Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 12
Year of Publication: 2021
Authors: Aditi Mehrolia, Aditya Patel

Aditi Mehrolia, Aditya Patel . Visibility Enhancement of Underwater Hazy Image using Multi-model SVD-DWT Fusion. International Journal of Computer Applications. 174, 12 ( Jan 2021), 17-20. DOI=10.5120/ijca2021920997

@article{ 10.5120/ijca2021920997,
author = { Aditi Mehrolia, Aditya Patel },
title = { Visibility Enhancement of Underwater Hazy Image using Multi-model SVD-DWT Fusion },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 12 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021920997 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:21:54.975367+05:30
%A Aditi Mehrolia
%A Aditya Patel
%T Visibility Enhancement of Underwater Hazy Image using Multi-model SVD-DWT Fusion
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 12
%P 17-20
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

Underwater hazy images (UHI) are inherently dark in nature and are affected by small suspending particles and marine snow. To increase the visibility range and vision depth, an artificial light is utilized. The rays of light are scattered by particles in the underwater medium and along with color attenuation results in problems such as contrast reduction, blurring of an image and color loss driving the images beyond recognition. In absence of any dehazing technique, the performance and usability of a standard enhancement algorithm may fail to produce desirable results. In this paper, we have proposed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. The fused image is then subjected to contrast stretching operation to improve the global contrast and visibility of dark regions.

  1. Rajni Sethi, “Fusion of Underwater Image Enhancement and Restoration”, International Journal of Pattern Recognition and Artificial Intelligence Vol. 34, No. 3 (2020) 2054007.
  2. H. Wang, R. Zhao, Y. Cen, L. Liang, Q. He, F. Zhang and M. Zeng, Low-rank matrix recovery via smooth rank function and its application in image restoration, Int. J. Mach. Learn. Cybernet. 9 (2018) 1565–1576.
  3. Y. Wang, J. Zhang, Y. Cao and Z. Wang, A deep cnn method for underwater image enhancement, in 2017 IEEE Int. Conf. Image Processing (ICIP) (Beijing, China, 2017), pp. 1382–1386.
  4. C. O. Ancuti, C. Ancuti, C. D. Vleeschouwer and P. Bekaert, Color balance and fusion for underwater image enhancement, IEEE Trans. Image Process. 27 (2018) 379–393.
  5. C. Ancuti, C. O. Ancuti, T. Haber and P. Bekaert, Enhancing underwater images and videos by fusion, in 2012 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2012), pp. 81–88.
  6. T. Çelebi and S. Ertürk, Visual enhancement of underwater images using empirical mode decomposition, Expert Syst. Appl. 39(1) (2012) 800–805.
  7. L. Chao and M. Wang, Removal of water scattering, 2010 Int. Conf. Computer Engineering and Technology, Proceedings ICCET 2010, Vol. 2 (Chengdu, China, 2010), pp. 35–39.
  8. J. Y. Chiang and Y. C. Chen, Underwater image enhancement by wavelength compensation and dehazing, IEEE Trans. Image Process. 21(4) (2012) 1756–1769.
  9. A. Duarte, F. Codevilla, J. D. O. Gaya and S. S. C. Botelho, A dataset to evaluate underwater image restoration methods, in OCEANS 2016, Shanghai, April 2016, pp. 1–6.
  10. A. Galdran, D. Pardo, A. Picón and A. Alvarez-Gila, Automatic red-channel underwater image restoration, J. Vis. Commun. Image Represent. 26 (2015) 132–145.
  11. C. Gao, J. Zhou, C. Liu and Q. Pu, Image enhancement based on fractional directional derivative, Int. J. Mach. Learn. Cybern. 6(1) (2015) 35–41.
  12. X. Wei, H. Wang, G. Guo and H. Wan, Multiplex image representation for enhanced recognition, Int. J. Mach. Learn. Cybernet. 9 (2018) 383–392.
  13. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva and A. Torralba, Sun database: Large-scale scene recognition from abbey to zoo, in 2010 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (San Francisco, CA, USA, June 2010), pp. 3485–3492.
  14. C. Yan, N. Sang and T. Zhang, Local entropy-based transition region extraction and thresholding, Pattern Recognit. Lett. 24(16) (2003) 2935–2941.
Index Terms

Computer Science
Information Sciences


Multi-model DWT SVD Global Contrast Visibility