Image denoising and detail preservation by probabilistic models

In this paper, we present a novel noise suppression and detail preservation algorithm. As a first step, the test image is pre-processed through a multiresolution analysis employing the discrete wavelet transform. Then, we design a fast and robust total variation technique, incorporating a statistical representation in the style of maximum likelihood estimation. Finally, we compare this proposed approach to current state-of-the-art denoising methods applied on synthetic and real images. The results demonstrate the encouraging performance of our algorithm.

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