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A Preprocessing Framework for Underwater Image Denoising

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A Preprocessing Framework for Underwater Image Denoising
A PREPROCESSING FRAMEWORK FOR AUTOMATIC UNDERWATER IMAGES DENOISING
*

U.Deepika III MCA

Dr.S.N.Geethalakshmi Associate Professor

Dr.P.Subashini Associate Professor

Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women University, Coimbatore, India. *mithilydeep@gmail.com

Abstract
A major obstacle to underwater operations using cameras comes from the light absorption and scattering by the marine environment, which limits the visibility distance up to a few meters in coastal waters. The preprocessing methods concentrate on contrast equalization to deal with nonuniform lighting caused by the back scattering. Some adaptive smoothing methods like anisotropic filtering as a lengthy computation time and the fact that diffusion constants must be manually tuned, wavelet filtering is faster and automatic. An adaptive smoothing method helps to address the remaining sources of noise and can significantly improve edge detection. In the proposed approach, wavelet filtering method is used in which the diffusion constant is tuned automatically. Keywords: underwater image, preprocessing, edge detection, wavelet filtering, denoising.

I. INTRODUCTION The underwater images usually suffers from non-uniform lighting, low contrast, blur and diminished colors. A few problems pertaining to underwater images are light absorption and the inherent structure of the sea, and also the effects of colour in underwater images. Reflection of the light varies greatly depending on the structure of the sea. Another main concern is related to the water that bends the light either to make crinkle patterns or to diffuse it. Most importantly, the quality of the water controls and influences the filtering properties of the water such as sprinkle of the dust in water. The reflected amount of light is partly polarised horizontally and partly enters the water vertically. Light attenuation limits the visibility distance at about twenty meters



References: [1] Arnold-Bos, J. P. Malkasse and Gilles Kervern,(2005) “Towards a model-free denoising of underwater optical image,” IEEE OCEANS 05 EUROPE,Vol.1, pp.234256. [2] Caefer, Charlene E.; Silverman, Jerry. &Mooney,JonathanM,(2000) “Optimisation of point target tracking filters”. IEEE Trans. Aerosp. Electron. Syst., pages 15-25. [3] R. Garcia, T. Nicosevici, and X. Cufi. (2002) “On the way to solve lighting problems in underwater imaging”. In Proceedings of the IEEE Oceans 2002, pages 1018–1024. [4] James C. Church, Yixin Chen, and Stephen V., (2008) “A Spatial Median Filter for Noise Removal in Digital Images”, page(s):618 – 623. [45 Jenny Rajan and M.R Kaimal., (2006) “Image Denoising Using Wavelet Embedded anisotropic Diffusion”, Appeared in the Proceedings of IEEE International Conference on Visual Information Engineering, page(s): 589 – 593. [6] Z. Liu, Y. Yu, K. Zhang, and H. Huang.,(2001) “Underwater image transmission and blurred image restoration”. SPIE Journal of Optical Engineering, 40(6):1125–1131. [7] P. Perona and J.Malik, (1990) “Scale space and edge detection using anisotropic diffusion,” IEEE Trans on Pattern Analysis and Machine Intelligence, pp.629-639. [8] Schechner, Y and Karpel, N., (2004) “Clear Underwater Vision”. Proceedings of the IEEE CVPR, Vol. 1, pp. 536-543. [9] Stephane Bazeille, Isabelle, Luc jaulin and Jean-Phillipe Malkasse, (2006) “Automatic Underwater image PreProcessing”, cmm’06 - characterisation du milieu marine page(s): 16-19. [10] Yongjian Yu and Scott T. Acton, (2002) "Speckle Reducing Anisotropic Diffusion", IEEE Transactions on Image Processing, page(s): 1260-1270, No. 11, Vol.11.

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