Journal ArticleDOI
SAR speckle reduction using wavelet denoising and Markov random field modeling
TLDR
Experimental results show that the proposed speckle reduction algorithm outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases and achieves better performance than the refined Lee filter.Abstract:
The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.read more
Citations
More filters
Journal ArticleDOI
Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights
TL;DR: The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades, which performs a weighted average of the values of similar pixels which depends on the noise distribution model.
Journal ArticleDOI
Automated breast cancer detection and classification using ultrasound images: A survey
TL;DR: Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification, and their advantages and disadvantages are discussed.
Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-Based Weights c 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
TL;DR: In this article, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model, and denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way.
Journal ArticleDOI
Improved Sigma Filter for Speckle Filtering of SAR Imagery
TL;DR: The bias problem is solved by redefining the sigma range based on the speckle probability density functions, and a target signature preservation technique is developed to mitigate the problems of blurring and depressing strong reflective scatterers.
Journal ArticleDOI
SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling
TL;DR: A novel Bayesian-based algorithm within the framework of wavelet analysis is proposed, which reduces speckle in SAR images while preserving the structural features and textural information of the scene.
References
More filters
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Maximum likelihood from incomplete data via the EM algorithm
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Handbook of Mathematical Functions
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
De-noising by soft-thresholding
TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.