Palm print image de-noising based on BEMD and wavelet packet transform-wiener filter

  • Authors:

  • Gui-Ping Dai

  • Affiliations:

  • Department of Electronic & Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu, China

  • Venue:
  • ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
  • Year:
  • 2011

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Abstract

A novel de-noising method based on BEMD (Bi-dimensional Empirical Mode Decomposition) and wavelet packet transform-wiener filter was proposed. Firstly, BEMD was applied to decompose the preprocessed palm print image including noise into a group of IMFs (Intrinsic Mode Functions) with different intrinsic time scales, and then the first several IMFs corresponding to high frequency information and noise were de-noised by means of wavelet packet decomposition integrated with wiener filter; finally, the image was reconstructed through adding the processed IMFs and the residual component. Simulation results show that compared with BEMD, wavelet packet threshold de-noising and BEMD integrated with wavelet threshold de-noising, this proposed method can achieve more superior de-noising performance with the lowest MSE and the highest PSNR, which provides a basis for the accurate extraction of palm print features.