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SAR image despeckling combining target detection with improved nonlocal means

机译:结合目标检测与改进的非局部均值的SAR图像去斑

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摘要

To effectively preserve targets such as edges and dots of synthetic aperture radar (SAR) images while cleanly removing noise, a despeckling strategy is proposed. Both despeckling results of bilateral filtering and an improved nonlocal means algorithm (INLMeans) are first utilised to detect targets. Then, the detected targets are removed from the SAR image and INLMeans is employed to denoise the targets-removed image. Finally, the removed targets are added to the despeckled targets-removed image. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform those of the anisotropic diffusion and INLMeans.
机译:为了有效地保护目标,例如合成孔径雷达(SAR)图像的边缘和点,同时干净地去除噪声,提出了一种去斑点策略。双边滤波的去斑点结果和改进的非局部均值算法(INLMeans)都首先用于检测目标。然后,从SAR图像中删除检测到的目标,并使用INLMeans对去除目标的图像进行降噪。最后,将去除的目标添加到去除斑点的目标中。实验结果表明,所提算法的视觉质量和评价指标均优于各向异性扩散和INLMeans。

著录项

  • 来源
    《Electronics Letters》 |2011年第12期|p.724-725|共2页
  • 作者

    Zhang W.G.; Zhang Q.;

  • 作者单位

    College of Geology Engineering and Geomatics, Chang'an University;

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  • 原文格式 PDF
  • 正文语种 eng
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