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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.