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. *email@example.com
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 in clear water and five meters or less in turbid water. Forward scattering generally leads to blur of the image features, backscattering generally limits the contrast of the images. The amount of light is reduced when we go deeper, colors drop off depending on their wavelengths. The blue color travels across the longest in the water due to its shortest
wavelength. Current preprocessing methods typically only concentrate on local contrast equalization in order to deal with the nonuniform lighting caused by the back scattering. II. UNDERWATER DEGRADATION A major difficulty to process underwater images comes from light attenuation. Light attenuation limits the visibility distance, at about twenty meters in clear water and five meters or less in turbid water. The light attenuation process is caused by the absorption (which removes light energy) and scattering (which changes the direction of light path). Absorption and scattering effects are due to the water itself and to other components such as dissolved organic matter or small observable floating particles. Dealing with this difficulty, underwater imaging faces to many problems: first the rapid attenuation of light requires attaching a light source to the vehicle providing the necessary lighting. Unfortunately, artificial lights tend to illuminate the scene in a non uniform fashion producing a bright spot in the center of the image and poorly illuminated area surrounding. Then the distance between the camera and the scene usually induced prominent blue or green color (the wavelength corresponding to the red color disappears in only few meters). Then, the floating particles highly variable in kind and concentration, increase absorption and scattering effects: they blur image features (forward scattering), modify colors and produce bright artifacts known as “marine snow”. At last the non stability of the
underwater vehicle affects once again image
To test the accuracy of the preprocessing...
References:  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.  Caefer, Charlene E.; Silverman, Jerry. &Mooney,JonathanM,(2000) “Optimisation of point target tracking filters”. IEEE Trans. Aerosp. Electron. Syst., pages 15-25.  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.  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.  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.  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.  Schechner, Y and Karpel, N., (2004) “Clear Underwater Vision”. Proceedings of the IEEE CVPR, Vol. 1, pp. 536-543.  Stephane Bazeille, Isabelle, Luc jaulin and Jean-Phillipe Malkasse, (2006) “Automatic Underwater image PreProcessing”, cmm’06 - characterisation du milieu marine page(s): 16-19.  Yongjian Yu and Scott T. Acton, (2002) "Speckle Reducing Anisotropic Diffusion", IEEE Transactions on Image Processing, page(s): 1260-1270, No. 11, Vol.11.
Please join StudyMode to read the full document