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SAR image despeckling using heavy-tailed Burr distribution

Content Provider	Springer Nature Link
Author	Sumaiya, M. N. Shantha Selva Kumari, R.
Copyright Year	2016
Abstract	Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.
Starting Page	49
Ending Page	55
Page Count	7
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	11
Issue Number	1
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2016-04-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Synthetic aperture radar image processing Maximum a posteriori (MAP) estimation Burr probability density function Mellin transform Multi-resolution wavelet Signal,Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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