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Simple and Effective Method to Remove Hazy Effect from Underwater Images

Riya Sharma, Shivani A Gupta, Bhupendra Gupta

Abstract


Underwater images usually suffer by the underwater hazy effect and color degra-dation caused by suspended particles such as minerals, sand and dust particles in the water. To recover underwater scene with actual colors, fine details and without hazy effect is a challenging task. In this work, we are presenting an easy and effective approach to haze effect removal from single underwater hazy images. The proposed approach comprises of three steps. Initially, color bal-ancing is used on underwater images to retrieve the actual color of underwater objects. Next step deal with the removal of the underwater hazy effect by using modified dark channel prior. Finally, local detail enhancement of the haze-free image is achieved by using discrete cosine transform (DCT). The effectiveness of the proposed work has been proven by comparing our results with some state-of-the-art-methods.

 


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References


Y.Y. Schechner and N. Karpel, Recovery of underwater visibility and struc-ture by polarization analysis, IEEE Journal of Oceanic Engineering, 5 (2005), Pp 570–587.

T. Treibitz and Y.Y. Schechner, Active polarization descattering, IEEE transactions on pattern analysis and machine intelligence, 31 (2009), Pp 385–399.

Y.Y. Schechner, S.G. Narasimha and S.K. Nayar, Instant dehazing of images using polarization, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1 (2001), Pp 325–332.

K. He, J. Sun, and X. Tang, Single image haze removal using dark channel prior, In IEEE CVPR, 2009.

K. He, J. Sun, and X. Tang, Single image haze removal using dark chan-nel prior, IEEE transactions on pattern analysis and machine intelligence, 33 (2010), Pp 2341–2353.

T. Luczynski, A. Birk, Underwater Image Haze Removal and Color Correc-tion with an Underwater-ready Dark Channel Prior. arXiv, 2018.

P. Drews Jr., E. do Nascimento, F. Moraes, S. Botelho, M. Campos, The IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, Pp 825–830.

C.O. Ancuti, C. Ancuti, C.D. Vleeschouwer, L. Neumann, R. Garca, Color transfer for underwater dehazing and depth estimation, In Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017, Pp 695–699.

A. Galdran, D. Pardo, A. Picn, A. Alvarez-Gila, Automatic Red-Channel underwater image restoration, Journal of Visual Communication and Image Representation, 26 (2015), Pp 132–145.

R. Fattal, Single image dehazing, ACM transactions on graphics (TOG), 27 (2008), Pp 1–9.

R. Fattal, Dehazing using color-lines, ACM transactions on graphics (TOG), 34 (2014), PP1–14.

D. Berman, S. Avidan, Non-local image dehazing, In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, Pp 1674–1682.

D. Berman, T. Treibitz, S. Avidan, Air-light estimation using haze-lines, In Proceedings of the 2017 IEEE International Conference on Computational Photography (ICCP), 2017, Pp 1–9.

S.S. Sankpal, S.S. Deshpande, Nonuniform Illumination Correction Al-gorithm forunderwater images Using Maximum Likelihood Estimation Method, Journal of Engineering, 2016.

J.M. Morel, A.B. Petro, and C. Sbert, Screened Poisson Equation for Image Contrast Enhancement, Image Processing On Line, 4 (2014), Pp 16–29.

N.C. Bianco, A. Mohan, R.M. Eustice, Initial results in underwater single image dehazing, In OCEANS 2010 MTS/IEEE SEATTLE, Pp 1–8. IEEE.

G. Bianco, M. Muzzupappa, F. Bruno, R. Garcia, L. Neumann, A new color correction method for underwater imaging, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40 (2015), Pp 25–32.

G. Bianco, L. Neumann, A fast enhancing method for non-uniformly illu-minated underwater images, In OCEANS 2017-Anchorage, 1–6. IEEE, 2017.

D.L. Ruderman, T.W. Cronin and C.C. Chiao, Statistics of cone responses to natural images: implications for visual coding. JOSA A, 15 (1998), Pp 2036–2045.

SM Pizer, EP Amburn, JD Austin, R Cromartie, A Geselowitz, T Greer, B ter Haar Romeny, JB Zimmerman, K Zuiderveld, Adaptive histogram equalization and its variations, Computer vision, graphics, and image processing,39(1987), Pp 355–68.

K. Zuiderveld, Contrast limited adaptive histogram equalization, In Graph-ics Gems IV; Academic Press Professional, Inc.: Cambridge, MA, USA, Pp 474–485, 1994.

R. Singh, M. Biswas, Contrast and color improvement based haze removal of underwater images using fusion technique, In Proceedings of the 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 138–143, 2017.

S. Anwar, C. Li, F. Porikli, Deep underwater image enhancement. arXiv preprint arXiv:1807.03528. 2018 Jul 10.

K. Nomura, D. Sugimura, T. Hamamoto, Underwater Image Color Cor-rection using Exposure-Bracketing Imaging, IEEE Signal Processing Letters 25 (2018), Pp 893–897.

C. Ancuti, C.O. Ancuti, T. Haber, P. Bekaert, Enhancing underwater im-ages and videos by fusion, In 2012 IEEE Conference on Computer Vision and Pattern Recognition, Pp 81–88. IEEE, 2012.

C.O. Ancuti, C. Ancuti, C.D. Vleeschouwer, P. Bekaert, Color Balance and Fusion for Underwater Image Enhancement, IEEE Tranactions on Image Processing, 27 (2018), Pp 379–393.

D.K. Jha, B. Gupta, S.S. Lamba, l2-norm-based prior for haze-removal from single image, IET Computer 10 (2016), Pp 331–343.

Xueyang Fu, Jiye Wang, Delu Zeng, Yue Huang, Xinghao Ding. Remote Sensing Image Enhancement Using Regularized-Histogram Equalization and DCT. IEEE Geoscience and Remote Sensing Letters. 12 (2015), Pp 2301–2305.

R.C. Gonzalez, R.E. Woods, Digital Image Processing, Upper Saddle River, NJ, USA: Prentice-Hall, 2006.

J. von Kries, Festschrift der Albrecht-Ludwigs-Universitt (Fribourg, 1902). InSources of color science 1970. MIT Press Cambridge.

Z. Wang, A.C. Bovik, A Universal Image Quality Index,IEEE signal pro-cessing letters, 9 (2002), Pp 81–84.

D. Hasler, S.E. Susstrunk, Measuring Colourfulness in Natural Images, In Human vision and electronic imaging VIII (Vol. 5007 (2003), Pp 87–95




DOI: https://doi.org/10.37591/jscrs.v7i1.1595

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