The coherent nature of ultrasound imaging inherently produces the notorious signal-dependent speckle noise. Recently, a novel approach based upon embedding the statistical and physical properties of speckle patterns into a Markov-random-field (MRF) framework was developed and demonstrated by the authors in the context
of synthetic-aperture radar imaging. The contributions of this work are twofold. First, the MRF approach is extended to include a pseudo maximum-likelihood estimator of a key model parameter, making the approach fully autonomous. Second, the capability of the extended approach, called the modified MRF-based conditional-expectation approach (MRFCEA), in denoising real ultrasound imagery is demonstrated. The proposed MRFCEA approach offers superior performance over existing methods by reducing speckle noise without
compromising the spatial resolution. In addition, MRFCEA is autonomous, contrary to existing methods such as the enhanced-Frost or the modified-Lee, which require user's input.
One of the major factors plaguing the performance of synthetic aperture radar (SAR) imagery is the presence of signal-dependent speckle noise. Grainy in appearance, speckle noise is primarily due to the phase fluctuations of the electromagnetic return signals. Since inherent spatial-correlation characteristics of speckle in SAR images are not exploited in existing multiplicative models for speckle noise, a new approach is proposed here that provides a new mathematical framework for modeling and reduction of speckle noise. The contribution of this paper is twofold. First, a novel model for speckled SAR imagery is introduced based on Markov random fields (MRFs) in conjunction with statistical optics. Second, utilizing the model, a global energy-minimization algorithm, based on simulated annealing (SA), is introduced for speckle reduction. In particular, the joint conditional probability density function (cpdf) of the intensity of any two points in the speckled image and the associated correlation function are used to derive the cpdf of any center pixel intensity given its four neighbors. The Hammersley-
Clifford theorem is then used to derive the energy function associated with the MRF. The SA, built on the Metropolis sampler, is employed for speckle reduction. Four metrics are used to assess the quality of the speckle reduction: the mean-square error, SNR, an edge-preservation parameter and the equivalent number of looks. A comparative study using both simulated speckled images as well as real SAR images indicates that the proposed approach performs favorably in comparison to existing filtering techniques such as the modified-Lee and the enhanced Frost-algorithms.