Home   >   CSC-OpenAccess Library   >    Manuscript Information
Computationally Efficient Methods for Sonar Image Denoising using Fractional Mask
Rithu James, Supriya M H
Pages - 249 - 258     |    Revised - 31-10-2016     |    Published - 01-12-2016
Volume - 10   Issue - 5    |    Publication Date - December 2016  Table of Contents
Speckle, Fractional Order, Heterogeneous, Patches, Homogeneity.
Sonar images produced due to the coherent nature of scattering phenomenon inherit a multiplicative component called speckle and contain almost homogeneous as well as textured regions with relatively rare edges. Speckle removal is a pre-processing step required in applications like the detection and classification of objects in the sonar image. In this paper computationally efficient Fractional Integral Mask algorithms to remove the speckle noise from sonar images is proposed. Riemann- Liouville definition of fractional calculus is used to create Fractional integral masks in eight directions. The use of a mask incorporated with the significant coefficients from the eight directional masks and a single convolution operation required in such case helps in obtaining the computational efficiency. The sonar image heterogeneous patch classification is based on a new proposed naive homogeneity index which depends on the texture strength of the patches and despeckling filters can be adjusted to these patches. The application of the mask convolution only to the selected patches again reduce the computational complexity. The non-homomorphic approach used in the proposed method avoids the undesired bias occurring in the traditional homomorphic approach. Experiments show that the mask size required directly depends on the fractional order. Mask size can be reduced for lower fractional orders thus ensuring the computation complexity reduction for lower orders. Experimental results substantiate the effectiveness of the despeckling method. The different non reference image performance evaluation criterion are used to evaluate the proposed method.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Lopès, R. Touzi, and E. Nezry, "Adaptive speckle filters and scene heterogeneity," IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 6, pp. 992-1000, Nov. 1990.
C. Mazel, "Side Scan Sonar Record Interpretation," Klein Assoc., Inc., 1985.
D. T. Kuan, A.A. Sawchuk, T.C.Strand, and P. Chavel. Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise.IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. PAMI-7, pp. 165-177, 1985.
David L. Donoho. Denoising via soft thresholding. IEEE Transactions on Information Theory, 41:613{627, May 1995.
Donato Cafagna. Fractional Calculus: A Mathematical Tool from the Past for Present Engineers. IEEE Industrial Electronics Magazine; 2007.p.1932- 4529.
J. Bigun, G. H. Granlund, and J. Wiklund. Multidimensional orientation estimation with applications to texture analysis and optical flow, IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 775790, Aug. 1991.
J. S.Lee. Digital image enhancement and noise filtering by use of local statistics.IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, pp. 165-168, 1980.
J. W.Goodman. Some fundamental properties of speckle J. Opt. Soc. Am., vol. 66, pp. 11451150,1976.
Jinrong HU, Yi-fei PU, Ji-Liu Zhou. A Novel Image Denoising Algorithm Based on Riemann-Liouville Definition. Journal of computers Vol.6(7); 2011.
Langis Gagnon. Wavelet Filtering of Speckle Noise-Some Numerical Results Proceedings of the Conference Vision Interface, Trois-Riveres,1999.
Liya Ancel and Rithu James. "Poisson Noise Removal from Medical Images using Fractional Integral Mask," presented at ICETT, Coimbatore, India, 2016.
Manuel Duarte Ortigueira. Riesz Potential Operators and Inverses via Fractional Centred Derivatives. International Journal of Mathematics and Mathematical Sciences; 2006.p.1-12.
Peter C. Wille. Sound Images of the Ocean in Research and Monitoring. Springer, 2005.
Philippe Blondel. The Handbook of Sidescan Sonar. Springer-Praxis Publishing, 2009.
R. J. Urick, Principles of Underwater Sound. New York: McGraw- Hill, 1975.
Rithu James and Supriya M H. "Despeckling of Sonar Images Based on a Naïve Homogeneity Index," presented at OCEANS/MTS, USA, 2016.
V.S. Frost, J.A. Stiles, K.S. Shanmugan, and J.C. Holtzmam. A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4, pp. 157-166, 1982.
X. Zhu and P. Milanfar. Automatic parameter selection for denoising algorithms using a no-reference measure of image content, IEEE Trans. Image Process., vol. 19, no. 12, pp. 311632, Dec. 2010.
Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Single-Image Noise Level Estimation for Blind Denoising IEEE Transactions on Image Processing, vol.22, No.12, December 2013.
Y. Yongjian and S. T. Acton. Speckle reducing anisotropic diffusion. IEEE Trans. Image Process., vol. 11, no. 11, pp. 12601270, Nov. 2002.
YI zhang, Yi-fei PU, Ji-Liu Zhou. Construction of Fractional differential Masks Based on Riemann-Liouville Definition. Journal of Computational Information Systems Vol.6(10); 2010.p.3191-3,1991.
Yi-fei PU, Ji-Liu Zhou, Xiao Yuan. Fractional Differential Mask: A Fractional Differential-Based Approach for Multiscale Texture Enhancement. IEEE Transactions on Image Processing Vol.19(2); 2010.p.491-511.
Mrs. Rithu James
CUSAT - India
Dr. Supriya M H
CUSAT - India