  Denoising is of utmost importance for the visualization and processing of
images featuring low signal-to-noise ratio. Total variation methods are among
the most popular techniques to perform this task improving the signal-to-noise
ratio while preserving coherent intensity discontinuities. In this work, a
novel method, termed maximum likelihood data, is proposed, endowing the total
variation formulation with the capability to deal with noise-specific models
and pre-processing stages for a certain image of interest. To do this, the data
fidelity term is modified by means of a maximum likelihood estimator between
the original and the denoised image. To assess the improvements of the proposed
method with respect to the total variation formulation, we study the denoising
of high-frequency ultrasonic images on in-silico and in-vivo setups. The
proposed method delivered a better contrast, preservation and localization of
the structures while diminishing the intensity bias of the total variation
formulation for the multiplicative noise. The enhancement of medical images
through denoising helps to improve the outcome of subsequently applied image
processing such as registration and segmentation procedures.
