  Modeling magnitude Magnetic Resonance Images (MRI) rician denoising in a
Bayesian or generalized Tikhonov framework using Total Variation (TV) leads
naturally to the consideration of nonlinear elliptic equations. These involve
the so called $1$-Laplacian operator and special care is needed to properly
formulate the problem. The rician statistics of the data are introduced through
a singular equation with a reaction term defined in terms of modified first
order Bessel functions. An existence theory is provided here together with
other qualitative properties of the solutions. Remarkably, each positive global
minimum of the associated functional is one of such solutions. Moreover, we
directly solve this non--smooth non--convex minimization problem using a
convergent Proximal Point Algorithm. Numerical results based on synthetic and
real MRI demonstrate a better performance of the proposed method when compared
to previous TV based models for rician denoising which regularize or convexify
the problem. Finally, an application on real Diffusion Tensor Images, a
strongly affected by rician noise MRI modality, is presented and discussed.
