Image Denoising Using Wavelet Thresholding and Model Selection

@inproceedings{Zhong2000ImageDU,
  title={Image Denoising Using Wavelet Thresholding and Model Selection},
  author={Shi Zhong and Vladimir Cherkassky},
  booktitle={ICIP},
  year={2000}
}
This paper describes wavelet thresholding for image denoising under the framework provided by Statistical Learning Theory aka Vapnik-Chervonenkis (VC) theory. Under the framework of VC-theory, wavelet thresholding amounts to ordering of wavelet coefficients according to their relevance to accurate function estimation, followed by discarding insignificant coefficients. Existing wavelet thresholding methods specify an ordering based on the coefficient magnitude, and use threshold(s) derived under… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-6 OF 6 REFERENCES

Image Denoising using Wavelet Thresholding and Statistical Leaming Theory

  • S. Zhong, V. Cherkassky
  • IEEE Trans. Image Processing,
  • 2000

Model Selection for Waveletbased Signal Estimation

  • X. Shao, V. Cherkassky
  • Proc. IEEE Int. Joint Con$ on Neural Networks,
  • 1998

Ideal spatial adaptation via wavelet thresholding

  • D. L. Donoho, I. M. Johnstone
  • Biometrika, vol
  • 1994

Wavelet Thresholding and W.V.D.: A 10minute Tour

  • D. L. Donoho
  • Int. Con$ on Wavelets and Applications,
  • 1992

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