SAR image despeckling using heavy-tailed Burr distribution
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- 作者：M. N. Sumaiya ; R. Shantha Selva Kumari
- 关键词：Synthetic aperture radar image processing ; Maximum a posteriori (MAP) estimation ; Burr probability density function ; Mellin transform ; Multi ; resolution wavelet
- 刊名：Signal, Image and Video Processing
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：11
- 期：1
- 页码：49-55
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems;
- 出版者：Springer London
- ISSN：1863-1711
- 卷排序：11
文摘
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.