Kalman's shrinkage for wavelet-based despeckling of SAR images
Computer Vision and Pattern Recognition
2016-08-03 v1
Abstract
In this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman's filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman's filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test synthetic aperture radar (SAR) images.
Keywords
Cite
@article{arxiv.1608.00273,
title = {Kalman's shrinkage for wavelet-based despeckling of SAR images},
author = {Mario Mastriani and Alberto E. Giraldez},
journal= {arXiv preprint arXiv:1608.00273},
year = {2016}
}
Comments
7 pages, 1 figure, 1 table. arXiv admin note: substantial text overlap with arXiv:1607.03105, arXiv:1608.00270, arXiv:1608.00279, arXiv:1608.00277, arXiv:1608.00274