Name
Abstract | ||
|---|---|---|
In this article we develop Stein-type results for unbiased estimation of the risk associated with parametric estimators of the noncentrality parameter of chi-squared random variables on two degrees of freedom. These results allow for estimator adaptivity, and thus can be used to optimize the parameters of a broad class of typical denoising functions, subject only to weak smoothness assumptions. We show how to apply these results to the problem of enhancing magnitude magnetic resonance images, which are known to be corrupted by Rician noise. As an example, we propose a transform-domain pointwise estimator based on linear expansion of thresholds. Finally, we apply this estimator to synthetic and real image data in conjunction with the undecimated Haar wavelet transform, and conclude that it is able to outperform previous wavelet-based techniques and compares favorably with a more recent approach based on non-local means. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6115745 | 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | Field | DocType |
Magnetic resonance, Image denoising, Rician noise, Chi-square, Unbiased MSE estimate, Shrinkage estimator | Minimum-variance unbiased estimator,Shrinkage estimator,Stein's unbiased risk estimate,Pattern recognition,Computer science,Artificial intelligence,Estimation theory,Haar wavelet,Wavelet,Estimator,Wavelet transform | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.43 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Luisier | 1 | 447 | 22.09 |
| Patrick J. Wolfe | 2 | 355 | 40.15 |