Removing mixed noise from remote sensing images by wavelet multifractal method

被引：0
|
作者：
Cheng L. [1 ]
Li A. [1 ]
Jia X. [1 ]
Li Z. [1 ]
机构：
[1] School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 15期
关键词：
image processing; multifractal; remote sensing image denoising; semi-soft threshold; wavelet analysis;
D O I：
10.37188/OPE.20223015.1880
中图分类号：
学科分类号：
摘要：
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. © 2022 Chinese Academy of Sciences. All rights reserved.
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页码：1880 / 1888
页数：8
相关论文
共 26 条
  • [21] PHAM C T, KOPYLOV A V., Multi-quadratic dynamic programming procedure of edge- preserving denoising for medical images［J］, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W6, 1, pp. 101-106, (2015)
  • [22] DEEPA MERLIN DIXON K, AJAY A, SOWMYA V, Et al., Aerial and satellite image denoising using least square weighted regularization method［J］, Indian Journal of Science and Technology, 9, 30, (2016)
  • [23] MAJI S K, THAKUR R K, YAHIA H M., Structure-preserving denoising of SAR images using multifractal feature analysis［J］, IEEE Geoscience and Remote Sensing Letters, 17, 12, pp. 2100-2104, (2020)
  • [24] MAJI S K, THAKUR R K, YAHIA H M., SAR image denoising based on multifractal feature analysis and TV regularisation［J］, IET Image Processing, 14, 16, pp. 4158-4167, (2020)
  • [25] NIU P J, MA X R, MAO R, Et al., Research on UAV image denoising effect based on improved Wavelet Threshold of BEMD［J］, Journal of Physics：Conference Series, 1437, 1, (2020)
  • [26] LI H F, YU B ZH, New wavelet-based algorithm for denoising of multifractal images［J］, Opt. Precision Eng, 12, 3, pp. 305-310, (2004)
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