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An Over-Complete Dictionary Design Based on GSR for SAR Image Despeckling

机译:基于GSR的SAR图像去斑的完全字典设计。

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In this letter, we explore the concept of group sparse representation (GSR) to exploit the intrinsic structure of synthetic aperture radar (SAR) image. Noting that dictionary design is a crucial factor in GSR performance, we propose an over-complete dictionary to better fit the SAR image despeckling problem. This over-complete dictionary consists of the prespecified dictionaries and learned dictionary. Different kinds of dictionaries emulate the image from different angles. In this way, we can simultaneously obtain better performance on speckle noise suppression and image detail preservation. The experimental results on real SAR images demonstrate that the proposed over-complete dictionary based on GSR can achieve more effective speckle reduction as well as image detail preservation.
机译:在这封信中,我们探讨了群体稀疏表示(GSR)的概念,以利用合成孔径雷达(SAR)图像的固有结构。注意到字典设计是GSR性能的关键因素,我们提出了一个过于完整的字典,以更好地拟合SAR图像去斑点问题。这本超完备的词典由预先指定的词典和学习词典组成。不同种类的词典从不同角度模拟图像。这样,我们就可以同时获得更好的斑点噪声抑制和图像细节保留性能。在真实SAR图像上的实验结果表明,基于GSR的超完备字典可以有效地减少斑点并保留图像细节。

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