Title: Minimal support tight wavelet frames in a probabilistic MRI denoising method
Authors: Sergio Villullas Merino - Universidad de Valladolid, Dpto de Algebra, Analisis Matematico, Geometria y Topologia (Spain) [presenting]
Abstract: Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the real Rician noise can be considered as Gaussian noise in high snr regions, and its wavelet frame coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled, in the wavelet frame domain, by a generalized Gaussian distribution with different fixed values of the parameter $\beta$ depending of the scale of the wavelet frame descomposition (general or multirresolution). The image denoising method proposed performs a shrinkage of wavelet frame coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's and non-local means filters, in different 2-dimensional images.