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A Machine Learning Framework for Adaptive Combination of Signal Denoising Methods

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2 Author(s)
David K. Hammond ; Center for Neural Science and Courant Institute of Mathematical Sciences, New York University ; Eero P. Simoncelli

We present a general framework for combination of two distinct local denoising methods. Interpolation between the two methods is controlled by a spatially varying decision function. Assuming the availability of clean training data, we formulate a learning problem for determining the decision function. As an example application we use Weighted Kernel Ridge Regression to solve this learning problem for a pair of wavelet-based image denoising algorithms, yielding a "hybrid" denoising algorithm whose performance surpasses that of either initial method.

Published in:

2007 IEEE International Conference on Image Processing  (Volume:6 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007