Denoising the Underwater Images by using Adaptive Filters
AbstractThe Sound Navigation And Ranging and synthetic aperture radar images are perturbed by the multiplicative noise called speckle noise . The presence of speckle noise leads to incorrect analysis and has to be handled carefully. Images have a strong variation from one pixel to another which reduces the efficiency of the algorithms for detection and classification. In this paper, the most well-known filters are analyzed by using underwater images. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Linear noise removing models can remove noise but are not able to preserve edges of the images in an efficient manner . On the other hand, Non-linear models can handle edges in a much better way than linear models. It is found that the linear filters and nonlinear filters can remove noise from small area objects and homogeneous areas but not in heterogeneous areas. Adaptive filters are used to remove noise not only from homogeneous area but also from heterogeneous areas. This paper presents an optimum filter by using locally estimated parameter values to provide minimum mean square error in order to smoothen the sonar images. It is also shown that the optimum filter is computationally efficient. The performance of this adaptive filter is compared (qualitatively) with other filters and found that the complexity of the frost filter is reduced.
(1) J.W.Goodman, “Some fundamental properties of speckle noise”, J.opt.Soc.am, vol.66,No.11, 1976, Pg.1145-1150
(2) Otis Lamont Frost,“An Algorithm For Linearly Constrained Adaptive Array Processing”, Proceedings of the IEEE, Vol. 60, No. 8, Aug 1972.
(3) J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no. 2, pp. 165–168, Feb. 1980.
(4) Victor S.Frost, Jsephine Abbott Stiles, K.S.Shanmugan and Julian C.Holtzman, “A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise”, IEEE Trans. Pattern Anal. Mach. Intell., vol.PAMI-4, no. 2, pp. 157–166, Feb. 1982.
(5) D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-7, no. 2, pp. 165–177, Feb. 1985.
(6) D.Kuan, A.Sawchuk, T.Strand, and P.Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.,vol. 35, no. 3, pp. 373–383, Mar. 1987.
(7) Armand Lopes, RidhaTouzi and E.Nezry, “Adaptive speckle filters and Scene Heterogeneity”, IEEE Transactions on geosciences and remote sensing, Vol.28,No 6,November 1990.
(8) A. Ben Hamza, P. Luque, J. Martinez, and R. Roman, “Removing noise and preserving details with relaxed median filters,” J. Math. Imag. Vision, vol. 11,no. 2, pp. 161–177, Oct. 1999.
(9) Er. Simrat, zEr. Anil Sagar, “Empirical Study of Various Speckle Noise Removal Methods”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), India,2014.
(10) Samuel Foucher,Goze Bertin Benie, and Jean-Marc Boucher, “Multiscale MAP Filtering of SAR Images”, IEEE Transactions on image processing, Vol. 10, No. 1, January 2001.
(11) Dr.G.Padmavathi, Dr.P.Subashini, Mr.M.Muthu Kumar and Suresh Kumar Thakur, “Performance analysis of Non Linear Filtering Algorithms for underwater images”, (IJCSIS) International Journal of Computer Science and Information
Security, Vol.6, No. 2, 2009.
(12) Patidar, Pawan, et al. “Image De-noising by Various Filters for Different Noise”, International Journal of Computer Applications 9.4 (2010): 45-50.
(13) Serge Karabchevsky, David Kahana, Ortal Ben-Harush, and Hugo Guterman , “FPGA-Based Adaptive Speckle Suppression Filter for Underwater Imaging Sonar”, IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 36, NO. 4, OCTOBER 2011.
(14) Yongjian Yu and Scott T. Acton, “Speckle Reducing Anisotropic Diffusion”, Senior Member,IEEE, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 11, NO. 11, NOVEMBER 2002.