Synthetic aperture radar (SAR) images are corrupted with speckle noise, which manifests as a multiplicative gamma noise and reduces the contrast in imagery, making detection and classifi- cation using SAR images a difficult task. Many speckle reduction techniques aim to reduce this noise without including available prior knowledge about the speckle and the scene contents. In this investigation, we develop a new technique for speckle reduction which incorporates both the statistical model of speckle and the a priori knowledge about the sparsity of edges present in the scene. Using the proposed technique, we despeckle a synthetic image, a SAR image from the MSTAR data set and a SAR image from the Gotcha data set. Our results show that, with our method, we are able to visually improve the quality of SAR images. We show quantitatively that we are able to reduce speckle in homogeneous areas beyond comparable methods, while maintaining edge and target intensity information.
Theresa Scarnati, Edmund Zelnio, and Christopher Paulson, "Exploiting the sparsity of edge information in synthetic aperture radar imagery for speckle reduction," Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 102010C (Presented at SPIE Defense + Security: April 13, 2017; Published: 28 April 2017); https://doi.org/10.1117/12.2267790.
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