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Image compressed sensing based on wavelet transform in contourlet domain~☆

机译:轮廓波域中基于小波变换的图像压缩感知〜☆

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摘要

Compressed sensing (CS) has been widely concerned and sparsity of a signal plays a crucial role in CS to exactly recover signals. Contourlet transform provides sparse representations for images, so an algorithm of CS reconstruction based on contourlet is considered. Meanwhile, taking into account the computation and the storage of large random measurement matrices in the CS framework, we are trying to introduce the wavelet transform into the contourlet domain to reduce the size of random measurement matrices. Several numerical experiments demonstrate that this idea is feasible. The proposed algorithm possesses the following advantages: reduced size of random measurement matrix and improved recovered performance.
机译:压缩感测(CS)已受到广泛关注,信号稀疏性在CS精确恢​​复信号中起着至关重要的作用。 Contourlet变换提供了图像的稀疏表示,因此考虑了基于Contourlet的CS重建算法。同时,考虑到CS框架中大型随机测量矩阵的计算和存储,我们试图将小波变换引入Contourlet域以减小随机测量矩阵的大小。几个数值实验表明,这种想法是可行的。该算法具有以下优点:减小了随机测量矩阵的大小,并提高了恢复性能。

著录项

  • 来源
    《Signal processing》 |2011年第5期|p.1085-1092|共8页
  • 作者单位

    School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China, Network Center, Xi Hua University, Chengdu 610039, China;

    School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;

    Drilling and Production Technology Research Institute, CNPC Chuanqing Drilling Engineering Company Limited, Cuanghan 618300, China;

    School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;

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

    compressed sensing; image reconstruction; contourlet domain; wavelet transform;

    机译:压缩感知;图像重建;轮廓波域;小波变换;

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