利用双树复小波和Shearlet变换降低SAR图像的斑点噪声

J. Deny, M. Sundarajan, R. Sudharsan, E. Muthukumaran, B. Perumal
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引用次数: 2

摘要

. 合成孔径雷达(SAR)图像由于相干采集系统产生的乘性散斑噪声而难以判读。因此,SAR图像的去斑处理一直是SAR图像处理的主要预处理任务。利用各种空间域滤波器和变换域算法进行散斑抑制的方法很多,但并不是所有的方法都能保留图像的边缘特征。本文提出了一种结合Shearlet变换和DTCW变换的稀疏表示去斑算法,该算法具有方向选择性和移位不变性。实验结果表明,该方法比现有方法具有更好的PSNR、ENL和EPI值。该方法不仅保留了SAR图像的边缘,而且通过增强图像的纹理来改善图像的视觉效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reduction of speckling noise of SAR Images using Dual Tree Complex Wavelet (DTCW) and Shearlet Transforms
. The Synthetic Aperture Radar (SAR) images are difficult to interpret owing to the multiplicative speckle noise from coherent acquisition systems. Therefore, despeckling of SAR images always plays a primary pre-processing task in SAR image processing. There are many methods using various spatial domain filters and transform domain algorithms which focus on speckle reduction but not all methods can preserve the image edge features. The article suggests a de-speckling algorithm via sparse representation using combined Shearlet Transform and DTCW transform which possess direction selectivity and shift invariant property. The experimental results, the suggested method has better PSNR, ENL, and EPI values than the existing state of art methods. The proposed methodology not only preserves the edges, also improves visual effect by enhancing the texture of SAR images.
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