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

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.


Published in:
Proceedings of the Thirty-Third IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'08), Las Vegas NV, USA, 905–908
Year:
2008
Publisher:
IEEE
Laboratories:




 Record created 2015-09-18, last modified 2018-12-03

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