Slicing the Transform - A Discriminative Approach for Wavelet Denoising

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 overcomplete 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.