Wavelet methods for inverting the Radon transform with noisy data

@article{Lee2001WaveletMF,
  title={Wavelet methods for inverting the Radon transform with noisy data},
  author={Nam-Yong Lee and Bradley J. Lucier},
  journal={IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
  year={2001},
  volume={10 1},
  pages={79-94}
}
Because the Radon transform is a smoothing transform, any noise in the Radon data becomes magnified when the inverse Radon transform is applied. Among the methods used to deal with this problem is the wavelet-vaguelette decomposition (WVD) coupled with wavelet shrinkage, as introduced by Donoho (1995). We extend several results of Donoho and others here. First, we introduce a new sufficient condition on wavelets to generate a WVD. For a general homogeneous operator, whose class includes the… CONTINUE READING

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