Removing mixed noise from remote sensing images by wavelet multifractal method

Libo CHENG; Angzhen LI; Xiaoning JIA; Zhe LI

doi:10.37188/OPE.20223015.1880

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Removing mixed noise from remote sensing images by wavelet multifractal method

Information Sciences | 更新时间：2022-08-16

- Removing mixed noise from remote sensing images by wavelet multifractal method
- Optics and Precision Engineering Vol. 30, Issue 15, Pages: 1880-1888(2022)
- 作者机构：
  
  长春理工大学数学与统计学院，吉林长春 130022
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223015.1880
  CLC： TP751
- Received：30 January 2022，
  
  Revised：19 March 2022，
  
  Published：10 August 2022
- 稿件说明：
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成丽波,李昂臻,贾小宁等.小波多重分形遥感图像混合去噪[J].光学精密工程,2022,30(15):1880-1888.

CHENG Libo,LI Angzhen,JIA Xiaoning,et al.Removing mixed noise from remote sensing images by wavelet multifractal method[J].Optics and Precision Engineering,2022,30(15):1880-1888.
成丽波,李昂臻,贾小宁等.小波多重分形遥感图像混合去噪[J].光学精密工程,2022,30(15):1880-1888. DOI： 10.37188/OPE.20223015.1880.

CHENG Libo,LI Angzhen,JIA Xiaoning,et al.Removing mixed noise from remote sensing images by wavelet multifractal method[J].Optics and Precision Engineering,2022,30(15):1880-1888. DOI： 10.37188/OPE.20223015.1880.

摘要

为了去除遥感图像的混合噪声，建立了小波多重分形去噪算法。该算法主要利用小波分析进行信号分解，多重分形提取图像特征。通过小波分解进行图像分解时，利用小波半软阈值指数衰减阈值法进行加性噪声初步处理。采用多重分形理论，找出含噪图像的多重分形谱，并构造偏移算子

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2.53999996

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对加性噪声进行二次处理。利用方向梯度与二维掩码层进行逐像素乘法获得稀疏梯度集，重建去噪图像。最后，计算出去噪图像的评价指标数值，根据数值分析评价去噪效果。实验结果表明：对6幅随机添加噪声的图片进行去噪，去噪图像的峰值信噪比最高为26.700

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3.72533321

2.37066650

，边缘保持指数最高为0.449。该方法能够有效去除遥感图像的混合噪声，基本满足遥感图像混合去噪的可视性，细节留存等要求。

Abstract

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.

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references

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