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Wavelet domain image denoising via support vector regression

Wavelet domain image denoising via support vector regression

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  • Author(s): H. Cheng 1 ; J.W. Tian 1 ; J. Liu 1 ; Q.Z. Yu 1
    • View affiliations
    • Affiliations: 1: Institute for Pattern Recognition and Artificial Intelligence, Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan, People's Republic of China
  • Source: Volume 40, Issue 23, 11 November 2004, p. 1479 – 1481
    DOI:  10.1049/el:20046567 , Print ISSN 0013-5194, Online ISSN 1350-911X
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An image denoising method based on wavelets and support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the performance of the presented method outperforms conventional wavelet denoising techniques.

Inspec keywords: wavelet transforms; image denoising; Gaussian noise; support vector machines; regression analysis

Other keywords: wavelet domain image denoising; Gaussian noise; real images; support vector regression; wavelet denoising techniques

Subjects: Computer vision and image processing techniques; Learning in AI (theory); Other topics in statistics; Other topics in statistics; Integral transforms; Optical, image and video signal processing; Integral transforms

References

    1. 1)
      • D.L. Donoho , I.M. Johnstone . Ideal spatial adaptation via wavelet shrinkage. Biometrica , 425 - 455
    2. 2)
      • V. Vapnik . (1995) The nature of statistical learning theory.
    3. 3)
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