In this paper, an algorithm for synthetic aperture radar (SAR) image denoising in the wavelet domain is presented. The
alpha-stable distribution is applied to model the wavelet coefficients of the logarithmically transformed SAR images and
the Gaussian mixture model to represent the Speckle. The method of regression-type is used to estimate the four
parameters of the alpha-stable distribution and EM algorithm to estimate the variance of the noise respectively. Since the
alpha-stable distribution do not always have a closed-form formula, Zolotarev's (M) parameterization is exploited to
obtain the probability density function (PDF) of the alpha-stable distribution. Consequently, a maximum a posteriori
(MAP) estimator is designed based on the alpha-stable prior to restore the SAR image. The experimental results,
including simulated SAR image and SIR-C/X-band SAR image, indicate that the proposed algorithm has capability both
in Speckle suppression and details preservation.
Propose one simple and efficient multi-level thresholding method. Basic dynamic is used to assess the reliability of thresholds. All possible thresholds are detected and sorted by assessment value calculated in water flooding process. Basing on the sorted threshold sequence, when level number changes, thresholds need not be recalculated, and multiple results can be got efficiently. Experimental results are satisfactory.
In this paper, an approach of edge detection based-on multifractal is proposed. We apply the 2D wavelet transform modulus maxima (WTMM) method to characterize pointwise Holder regularity and the multifractal spectrum, so edge information can be extracted directly from them. Experiment results demonstrate that multifractal based edge detection has strong flexibility and good detection effect.
In this paper, we present an unsupervised texture segmentation algorithm for Synthetic aperture radar (SAR) images based on a multiscale modeling over images in wavelet pyramidal structure. An image consisting of different textures can be considered as a realization of a collection of two interacting random process-the hidden region label process and the observation process. A novel Gaussian Markov random field (GMRF) model is proposed to describe the fill-in of regions at each scale and a multi-level logistic (MLL) MRF model with particular cliques is used to characterize the intrascale and interscale context dependencies. According to sequential maximum a posterior (SMAP) estimate, expectation-maximization (EM) algorithm is adopted to estimate the parameters of GMRF and to label each pixel iteratively from coarse to fine level. The proposed segmentation approach is applied to synthetic image and SAR image and the result shows its performance.
Based on the multi-resolution wavelet analysis, three thresholding methods: soft-thresholding, hard thresholding, and the high-pass thresholding are studied in this paper. The proposed high-pass thresholding method is a new effective algorithm for suppressing speckle in synthetic aperture radar (SAR) images. The method suppresses speckle by applying a high-pass function to process the amplitude of each detail image of the wavelet subspaces. The threshold of the function is novel and is computed by the maximal amplitude, the decomposition level p-th of the detail image. Application to SAR images has shown that the wavelet domain filtering methods promise a good tradeoff between speckle removal and edge protection. And the new high-pass thresholding method is more satisfactory in both speckle suppression and detail information preservation, and hence may provide better detection performance for SAR based recognition.