Name
Abstract | ||
|---|---|---|
We propose a novel deconvolution algorithm based on the minimization of Stein's unbiased risk estimate (SURE). We linearly parametrize the deconvolution process by using multiple Wiener filterings as elementary functions, followed by undecimated Haar-wavelet thresholding. The key contributions of our approach are: 1) the linear combination of several Wiener filters with different (but fixed) regularization parameters, which avoids the manual adjustment of a single nonlinear parameter; 2) the use of linear parameterization, which makes the SURE minimization finally boil down to solving a linear system of equations, leading to a very fast and exact optimization of the whole deconvolution process. The results obtained on standard test images show that our algorithm favorably compares with the other state-of-the-art deconvolution methods in both speed and quality. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467540 | ICIP |
Keywords | Field | DocType |
undecimated haar-wavelet thresholding,wiener filtering,undecimated haar wavelet thresholding,deconvolution,sure minimization,wavelet transforms,regularization parameters,stein's unbiased risk estimate minimization,linear parameterization,image segmentation,deconvolution algorithm,wiener filters,fast optimization,sure-let image deconvolution,multiple wiener filters,nonlinear parameter adjustment,exact optimization,elementary functions,haar transforms,linear parametrization,minimisation | Wiener filter,Linear combination,Nonlinear system,Pattern recognition,Blind deconvolution,System of linear equations,Computer science,Wiener deconvolution,Deconvolution,Artificial intelligence,Thresholding | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Xue | 1 | 15 | 6.03 |
| F. Luisier | 2 | 447 | 22.09 |
| T Blu | 3 | 2574 | 259.70 |