Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling

作者：Glaister Jeffrey^*; Wong Alexander; Clausi David A

来源：IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1238-1248.

DOI：10.1109/TGRS.2013.2248739

• Fisher-Tippett noise
• noise reduction
• speckle noise
• synthetic aperture radar (SAR)

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 Fisher-Tippett 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.

• 出版日期2014-2