The Role of Redundant Bases and Shrinkage Functions in Image Denoising

@article{HelOr2021TheRO,
  title={The Role of Redundant Bases and Shrinkage Functions in Image Denoising},
  author={Y. Hel-Or and Gil Ben-Artzi},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  volume={30},
  pages={3778-3792}
}
  • Y. Hel-Or, Gil Ben-Artzi
  • Published 2021
  • Engineering, Computer Science, Medicine
  • IEEE Transactions on Image Processing
Wavelet denoising is a classical and effective approach for reducing noise in images and signals. Suggested in 1994, this approach is carried out by rectifying the coefficients of a noisy image, in the transform domain, using a set of shrinkage functions (SFs). A plethora of papers deals with the optimal shape of the SFs and the transform used. For example, it is widely known that applying SFs in a redundant basis improves the results. However, it is barely known that the shape of the SFs… Expand

References

SHOWING 1-10 OF 44 REFERENCES
Bayesian Denoising of Visual Images in the Wavelet Domain
Image Denoising with Shrinkage and Redundant Representations
Optimal Denoising in Redundant Representations
A Discriminative Approach for Wavelet Denoising
  • Y. Hel-Or, D. Shaked
  • Mathematics, Computer Science
  • IEEE Transactions on Image Processing
  • 2008
Ridgelet decomposition: discrete implementation and color denoising
The curvelet transform for image denoising
Slicing the Transform-A Discriminative Approach for Wavelet Denoising
Why Simple Shrinkage Is Still Relevant for Redundant Representations?
  • Michael Elad
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 2006
The contourlet transform: an efficient directional multiresolution image representation
  • M. Do, M. Vetterli
  • Mathematics, Computer Science
  • IEEE Transactions on Image Processing
  • 2005
On denoising and best signal representation
...
1
2
3
4
5
...