Name
Title | ||
|---|---|---|
Monte-Carlo sure: a black-box optimization of regularization parameters for general denoising algorithms. |
Abstract | ||
|---|---|---|
We consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white Gaussian noise. To achieve this, we propose to minimize Stein's unbiased risk estimate (SURE) which provides a means of assessing the true mean-squared error (MSE) purely from the measured data without need for any knowledge about the noise-free signal. Specifically, we present a novel Monte-Carlo technique which enables the user to calculate SURE for an arbitrary denoising algorithm characterized by some specific parameter setting. Our method is a black-box approach which solely uses the response of the denoising operator to additional input noise and does not ask for any information about its functional form. This, therefore, permits the use of SURE for optimization of a wide variety of denoising algorithms. We justify our claims by presenting experimental results for SURE-based optimization of a series of popular image-denoising algorithms such as total-variation denoising, wavelet soft-thresholding, and Wiener filtering/smoothing splines. In the process, we also compare the performance of these methods. We demonstrate numerically that SURE computed using the new approach accurately predicts the true MSE for all the considered algorithms. We also show that SURE uncovers the optimal values of the parameters in all cases. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TIP.2008.2001404 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Gaussian noise,Monte Carlo methods,image denoising,image restoration,mean square error methods,optimisation,risk analysis,Monte-Carlo SURE,Stein unbiased risk estimate,black-box optimization,corrupted signal restoration,image-denoising algorithm,mean-squared error,regularization parameter,signal denoising algorithm,white Gaussian noise,Monte-Carlo methods,Stein's unbiased risk estimate (SURE),regularization parameter,smoothing splines,total-variation denoising,wavelet denoising | Noise reduction,Wiener filter,Mathematical optimization,Noise measurement,Algorithm,Mean squared error,White noise,Total variation denoising,Gaussian noise,Additive white Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 9 | 1057-7149 |
Citations | PageRank | References |
111 | 5.18 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sathish Ramani | 1 | 386 | 22.20 |
| T Blu | 2 | 2574 | 259.70 |
| Unser, M. | 3 | 3438 | 442.40 |