• DocumentCode
    2797551
  • Title

    A New Method for Sparse Signal Denoising Based on Compressed Sensing

  • Author

    Zhu, Lei ; Zhu, Yaolin ; Mao, Huan ; Gu, Meihua

  • Author_Institution
    Elecronics & Inf. Coll., Xi´´an Polytech. Univ., Xi´´an, China
  • Volume
    1
  • fYear
    2009
  • fDate
    Nov. 30 2009-Dec. 1 2009
  • Firstpage
    35
  • Lastpage
    38
  • Abstract
    Whether it is classic filter denoising, Fourier transform denoising, or the emerging wavelet transform denoising, a common characteristic is that denoising is only limited to some transform field. Also, these denoising methods are influenced greatly by the change of signal parameters, such as frequency, amplitude, etc. In order to effectively overcome the above-mentioned shortcomings of these denoising methods, this paper first introduces the basic principle of compressed sensing, and proposes a new compressed sensing denoising method to eliminate the noise in sparse signal according to the relationship between compressed sensing and signal sparsity. Then, the basic idea and algorithm steps of the new method is given, and an improved algorithm with better denoising effect is proposed aiming at the peak distortion produced by compressed sensing denoising method. At last, the denoising effect of the new method is validated by simulation experiment.
  • Keywords
    Fourier transforms; signal denoising; wavelet transforms; Fourier transform denoising; compressed sensing denoising method; filter denoising; signal sparsity; sparse signal denoising; wavelet transform denoising; Compressed sensing; Fluctuations; Fourier transforms; Information filtering; Information filters; Noise reduction; Signal denoising; Signal processing; Signal sampling; Wavelet transforms; compressed sensing; denoising; peak distortion; sparse signal; transform domain;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3888-4
  • Type

    conf

  • DOI
    10.1109/KAM.2009.25
  • Filename
    5362194