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SAR Image Despeckling by Using Nonlocal Sparse Coding Model

机译:非局部稀疏编码模型的SAR图像去斑

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摘要

Based on the key observation that the coding residuals between the recovered sparse codes of the noisy SAR image and those of the clean SAR image are sparse, we propose a sparse representation-based despeckling algorithm for SAR image. As the sparse codes of the clean SAR image are not available, the rich nonlocal repetitive structures of the logarithmic SAR images are exploited. To collect the similar patches within the logarithmic SAR image, an adaptive similarity evaluation obeying statistical distribution of the logarithmic speckle noise is derived. Experimental results on both synthetic and real SAR images demonstrate the validity of the proposed algorithm.
机译:基于关键的观察,即噪声SAR图像的恢复后的稀疏码与干净SAR图像的恢复后的稀疏码之间的编码残差是稀疏的,我们提出了一种基于稀疏表示的SAR图像去斑算法。由于没有清晰的SAR图像的稀疏代码,因此利用了对数SAR图像的丰富的非局部重复结构。为了在对数SAR图像中收集相似的补丁,导出了服从对数散斑噪声统计分布的自适应相似性评估。在合成和真实SAR图像上的实验结果证明了该算法的有效性。

著录项

  • 来源
    《Circuits, systems, and signal processing》 |2018年第7期|3023-3045|共23页
  • 作者单位

    Beijing Aerosp Automat Control Inst, Natl Key Lab Sci & Technol Aerosp Intelligence Co, Beijing 100854, Peoples R China;

    Beijing Aerosp Automat Control Inst, Natl Key Lab Sci & Technol Aerosp Intelligence Co, Beijing 100854, Peoples R China;

    Beijing Aerosp Automat Control Inst, Natl Key Lab Sci & Technol Aerosp Intelligence Co, Beijing 100854, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sparse coding; Nonlocal sparse representation; SAR despeckling (denoising);

    机译:稀疏编码;非局部稀疏表示;SAR去斑(去噪);
  • 入库时间 2022-08-18 01:14:13

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