Underwater Image Restoration Based on Illumination Normalization and Deblurring
DOI:
https://doi.org/10.26438/ijcse/v6i5.288296Keywords:
Image Restoration, Illumination Direction, Illumination Normalization, Deblurring, DeconvolutionAbstract
The fundamental reason for submerged image handling is to enhance submerged image enhancement. The preparing of submerged image caught is essential in light of the fact that the nature of submerged images influence and these images drives some significant issues when contrasted with images from a clearer domain. Because of the presence of clean particles in the water, submerged images suffer from the backscattering impact. To overcome this drawback I propose the new method called illumination normalization and deblurring of underwater image restoration. In this paper propose, first estimate the illumination directions of underwater images and cope with the problem of illumination normalization. Secondly deblurring of the underwater image using deconvolution algorithm and finally by the fusing both the results the restored image is acquired.The quality of the enhanced image is evaluated by using the metric is called blind/reference less image spatial quality evaluator (BRISQUE).
References
Bidyut Saha, “A Comparative Analysis of Histogram Equalization Based Image Enhancement Technique for Brightness Preservation”, International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.3, pp.1-5, 2015.
Hussam Elbehiery, “Optical Fiber Cables Networks Defects Detection using Thermal Images Enhancement Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.22- 29, 2018.
J. Chiang, Y. Chen, “Underwater Image Enhancement by Wavelength Compensation and Dehazing,” IEEE Transaction on Image Processing, vol. 21, Issue.4, pp. 1756–1769, 2012.
K. He., J. Sun, X. Tang, "Single Image Haze Removal Using Dark Channel Prior, "IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, Issue. 12, pp. 2341-2353, 2011.
Adrian Galdran, David Pardo, ArtzaiPicón, and Aitor Alvarez-Gila, “Automatic Red-Channel underwater image restoration”, Science Direct, J. Vis. Commun. Image R. 26132–145, 2015.
H. Wen, Y.Tian, T. Huang, W. Gao, “Single underwater image enhancement with a new optical model,” Proc. IEEE Int. Symp. Circ. & Syst. (ISCAS), pp. 753-756, 2013.
Xinwei Zhao, Tao Jin, Song Qu, “Deriving inherent optical properties from background color and underwater image enhancement”, Lists available at Science Direct, Ocean Engineering 94163–172, 2015.
R. Sathya, M. Bharathi, G. Dhivyasri, “Underwater Image Enhancement by Dark Channel Prior”, IEEE sponsored 2nd International Conference on Electronics and Communication System (ICECS), 2015.
C.Y. Cheng, C.C. Sung, H.H. Chang, "Underwater image restoration by red-dark channel prior and point spread function deconvolution," IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, pp. 110-115, 2015.
C. Ancuti, C.O. Ancuti, T. Haber, P. Bekaert, "Enhancing underwater images and videos by fusion, " IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 81- 88, 2012.
N. Carlevaris-Bianco, A. Mohan, R.M. Eustice, “Initial results in underwater single image dehazing”, Proc. IEEE Oceans, pp. 1-8, 2010.
HuiminLu, Seiichi Serikawa, “Underwater Scene Enhancement Using Weighted Guided Median Filter”, Marine Technology Society Journal, vol.42, Issue.1, pp.52-67, 2014.
Yan-TsungPeng, Xiangyun Zhao, Pamela C. Cosman, “Single Underwater Image Enhancement Using Depth Estimation based on Blurriness”, IEEE International Conference on Image Processing (ICIP), pp.4952- 4956, 2015.
X. Fu, P. Zhuang, Y. Huang, Y. Liao, X.P. Zhang, X. Ding, “A retinex-based enhancing approach for single underwater image”, in: IEEE Int. Conf. Image Process (ICIP), pp. 4572–4576, 2014.
K. Seemakurthy, A.N. Rajagopalan, "Deskewing of Underwater Images, "IEEE Transactions on Image Processing, vol. 24, no. 3, pp.1046- 1059, 2015.
Q. Zhu, J. Mai, L. Shao, "A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior, "IEEE Transactions on Image Processing, vol. 24, Issue. 11, pp. 3522-3533, 2015.
Lei Fei and Wang Yingying, “The Research of Underwater Image De- noising Method Based on Adaptive Wavelet Transform”, IEEE Conference,pp.2521-2525, 2014.
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