Acta Metallurgica Sinica(English letters)

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Wavelet domain denoising method based on multistage median filtering

吴进   

  1. 西安邮电大学
  • 收稿日期:2012-08-14 修回日期:2012-12-08 出版日期:2013-04-30 发布日期:2013-04-26
  • 通讯作者: 吴进 E-mail:huatao2000@126.com
  • 基金资助:

    This work was supported by the National Natural Science Foundation of China (61272120), and the Young Scholars Plan Project of Xi’an University of Posts and Telecommunications (ZL2012-11).

Wavelet domain denoising method based on multistage median filtering

  • Received:2012-08-14 Revised:2012-12-08 Online:2013-04-30 Published:2013-04-26
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (61272120), and the Young Scholars Plan Project of Xi’an University of Posts and Telecommunications (ZL2012-11).

摘要:

There are two main problems in the threshold denoising method based on wavelet transform. One is the difficulty of threshold selection, and the other is the inconsistence of the dip and curved events in the low signal-to-noise ratio (SNR) seismic data after denoising. In image denoising, multistage median filtering can preserve the details of the signal. So we proposed a denoising algorithm in wavelet transform domain based on multistage median filtering. Using this method the flat region and the edge region are differentiated by the difference between the maximum mid-value and the minimum mid-value, which preserves the details, thus improves the denoising effect. The simulation data and the real data processing results reveal that this method has stronger ability in separating signal from noise than that of the threshold denoising method.

关键词:

wavelet transform, multistage median filtering, denoising, threshold

Abstract:

There are two main problems in the threshold denoising method based on wavelet transform. One is the difficulty of threshold selection, and the other is the inconsistence of the dip and curved events in the low signal-to-noise ratio (SNR) seismic data after denoising. In image denoising, multistage median filtering can preserve the details of the signal. So we proposed a denoising algorithm in wavelet transform domain based on multistage median filtering. Using this method the flat region and the edge region are differentiated by the difference between the maximum mid-value and the minimum mid-value, which preserves the details, thus improves the denoising effect. The simulation data and the real data processing results reveal that this method has stronger ability in separating signal from noise than that of the threshold denoising method.

Key words:

wavelet transform, multistage median filtering, denoising, threshold