Blind optimization of algorithm parameters for signal denoising by Monte-Carlo sure

Thierry Blu, Sathish Ramani, Michaël Unser
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa, 2008
Article de conférence

Résumé

We consider the problem of optimizing the parameters of an arbitrary denoising algorithm by minimizing Stein's Unbiased Risk Estimate (SURE) which provides a means of assessing the true mean-squared-error (MSE) purely from the measured data assuming that it is corrupted by Gaussian noise. To accomplish this, we propose a novel Monte-Carlo technique based on a black-box approach which enables the user to compute SURE for an arbitrary denoising algorithm with some specific parameter setting. Our method only requires the response of the denoising algorithm to additional input noise and does not ask for any information about the functional. form of the corresponding denoising operator. This, therefore, permits SURE-based optimization of a wide variety of denoising algorithms (global-iterative, pointwise, etc). We present experimental results to justify our claims.

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https://infoscience.epfl.ch/record/160791?ln=fr

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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.

IEEE2008

Chargement

Blind Optimization of Algorithm Parameters for Signal Denoising by Monte-Carlo SURE

Thierry Blu, Sathish Ramani, Michaël Unser

We consider the problem of optimizing the parameters of an arbitrary denoising algorithm by minimizing Stein's Unbiased Risk Estimate (SURE) which provides a means of assessing the true mean-squared-error (MSE) purely from the measured data assuming that it is corrupted by Gaussian noise. To accomplish this, we propose a novel Monte-Carlo technique based on a black-box approach which enables the user to compute SURE for an arbitrary denoising algorithm with some specific parameter setting. Our method only requires the response of the denoising algorithm to additional input noise and does not ask for any information about the functional form of the corresponding denoising operator. This, therefore, permits SURE-based optimization of a wide variety of denoising algorithms (global-iterative, pointwise, etc). We present experimental results to justify our claims.

IEEE2008

Chargement