Optics and Precision Engineering, Volume. 30, Issue 15, 1880(2022)

Removing mixed noise from remote sensing images by wavelet multifractal method

Libo CHENG, Angzhen LI*, Xiaoning JIA, and Zhe LI
Author Affiliations
  • School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun130022, China
  • show less

    To remove the mixed noise from remote sensing images, a wavelet multifractal denoising algorithm was developed. The algorithm mainly uses wavelet analysis for signal decomposition and multifractal to extract image features. First, image decomposition was performed by wavelet decomposition, and additive noise was preliminarily processed using the exponential decay threshold method of the wavelet semi-soft threshold. Second, using the multifractal theory, the multifractal spectrum of the noisy image was found, and an offset operator is constructed to process the additive noise twice. Then, the sparse gradient set was obtained by multiplying the direction gradient with the two-dimensional mask layer pixel by pixel, and the denoised image is reconstructed. Finally, the evaluation index value of the denoised image was calculated, and the denoising effect was evaluated according to the numerical analysis. The experimental results show that the method can effectively remove the mixed noise of remote sensing images. The maximum peak signal-to-noise ratio of the denoised images is 26.700 dB by denoising six randomly added noise images. Moreover, the highest edge preservation index is 0.449. It can meet the requirements of the visibility and detail preservation of mixed denoising of remote sensing images and provide a reliable basis for subsequent analysis.

    Tools

    Get Citation

    Copy Citation Text

    Libo CHENG, Angzhen LI, Xiaoning JIA, Zhe LI. Removing mixed noise from remote sensing images by wavelet multifractal method[J]. Optics and Precision Engineering, 2022, 30(15): 1880

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Information Sciences

    Received: Jan. 30, 2022

    Accepted: --

    Published Online: Sep. 7, 2022

    The Author Email: Angzhen LI (liyuhaoxy@sina.com)

    DOI:10.37188/OPE.20223015.1880

    Topics