The SURE-LET Approach to Image Denoising

By Thierry Blu, Florian Luisier. IEEE Transactions on Image Processing, 2007

415 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics, Media Technology

View Paper: https://www.semanticscholar.org/paper/9bc8f0db0d268357eb44c921a213b68169bfbae5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Thierry Blu
2. Michael A. Unser
3. Florian Luisier
4. Sathish Ramani
5. G. Peyr'e
6. Charles-Alban Deledalle
7. Mohamed-Jalal Fadili
8. Samuel Vaiter

In Geophysics

1. Michael A. Unser
2. Sathish Ramani
3. Thierry Blu
4. Mohamed-Jalal Fadili
5. G. Peyr'e

In Media Technology

1. Thierry Blu
2. Michael A. Unser
3. Florian Luisier
4. Sathish Ramani
5. Mohamed-Jalal Fadili
6. G. Peyr'e
7. Charles-Alban Deledalle
8. Peyman Milanfar

Citations in the corpus

• Image Denoising in Mixed Poisson–Gaussian Noise (2011, background): “Because we are dealing with transformations that are not necessarily orthonormal anymore, the denoising process has to be optimized in the image domain, to ensure a global MSE minimization [3], [33].”
• Multi-Wiener SURE-LET Deconvolution (2013, neutral): “Other thresholding functions, such as the ones reported in [52], [54], do not result in significant PSNR loss (maximum 0.2 dB loss).”
• Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection (2014, background): “, [4, 6, 11, 17, 23, 48, 52, 54].”
• Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms (2008, methodology): “Interestingly enough, the result is not penalized by the lower order of the H aar transform (piecewise-constant approximation), in fact, it is quite the contrary (as was als o noticed in [20]).”