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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.
46 Pages
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