@article {80,
	title = {Despeckling of Synthetic Aperture Radar Images using Monte Carlo Texture Likelihood Sampling},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2014},
	abstract = {<p>Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a FisherTippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms.</p>
},
	keywords = {Fisher-Tippett noise, noise reduction, speckle noise, synthetic aperture radar},
	author = {J Glaister and A Wong and D A. Clausi}
}
