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Slicing the Transform - A Discriminative Approach for Wavelet Denoising

Hel-Or, Yacov; Shaked, Doron

HPL-2006-103R1

Keyword(s): denoising; wavelet; shrinkage

Abstract: This paper suggests a discriminative approach for wavelet denoising where a set of shrinkage functions (SF) are designed to perform optimally (in a MSE sense) with respect to a given set of images. Using the suggested scheme a new set of SFs are generated which are different from the traditional soft/hard thresholding in the over- complete case. These SFs are demonstrated to obtain the state-of-the-art denoising performance. As opposed to the descriptive approaches modeling image or noise priors are not required here and the SFs are learned directly from an ensemble of example images.

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