Abstract
In this paper, we relax the usual assumptions in denoising that the data consist of a true signal to which normally distributed noise is added. Instead of regarding noise as the high-frequency part in the data to be removed either by a hard or soft threshold, we define it as that part in the data which is harder to compress than the rest with the class of models considered. Here, we model the data by two histograms: one for the denoised signal and the other for the noise, both represented by wavelet coefficients. A code length can be calculated for each part, and by the principle of minimum description length the optimal decomposition results by minimization of the sum of the two code lengths
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