A machine learning framework for adaptive combination of signal denoising methods

D Hammond and E P Simoncelli

Presented at:
14th IEEE Int'l Conference on Image Processing

Published in Proc 14th IEEE Int'l Conf on Image Proc, vol.VI pp. 29--32, Sep 2007.
DOI: 10.1109/ICIP.2007.4379513

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    • 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.
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