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首页> 外文期刊>Computer and information science >SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain
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SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain

机译:非子样本Shearlet域中基于GNL-Means优化像素加权的SAR图像降噪

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摘要

SAR images have been widely used in many fields such as military and remote sensing. So the suppression of the speckle has been an important research issues. To improve the visual effect of non-local means, generalized non-local (GNL) means with optimized pixel-wise weighting is applied to shrink the coefficients of non-subsample Shearlet transform (NSST) of SAR image. The new method can optimize the weight of GNL, which not only improve the PSNR of de-noised image, but also can significantly enhance the visual effect of de-noising image.
机译:SAR图像已广泛用于军事和遥感等许多领域。因此,抑制斑点已经成为重要的研究课题。为了提高非局部均值的视觉效果,应用具有优化像素权重的广义非局部(GNL)均值来缩小SAR图像的非子样本Shearlet变换(NSST)的系数。新方法可以优化GNL的权重,不仅可以提高去噪图像的PSNR,而且可以显着增强去噪图像的视觉效果。

著录项

  • 来源
    《Computer and information science》 |2017年第1期|16-22|共7页
  • 作者单位

    College o Electronic and Information Engineering, Hebei University, China,Key Laboratory of Digital Medical Engineering of Hebei Province, China;

    College o Electronic and Information Engineering, Hebei University, China,Key Laboratory of Digital Medical Engineering of Hebei Province, China;

    College o Electronic and Information Engineering, Hebei University, China,Key Laboratory of Digital Medical Engineering of Hebei Province, China;

    Hebei University Department of Personnel, Baoding 071002, China;

    College o Electronic and Information Engineering, Hebei University, China,Key Laboratory of Digital Medical Engineering of Hebei Province, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    SAR image de-noising; NSST; GNL; optimized pixel-wise weighting;

    机译:SAR图像降噪;NSST;GNL;优化的逐像素加权;

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