  Homodyned K (HK) distribution has been widely used to describe the scattering
phenomena arising in various research fields, such as ultrasound imaging or
optics. In this work, we propose a machine learning based approach to the
estimation of the HK distribution parameters. We develop neural networks that
can estimate the HK distribution parameters based on the signal-to-noise ratio,
skewness and kurtosis calculated using fractional-order moments. Compared to
the previous approaches, we consider the orders of the moments as trainable
variables that can be optimized along with the network weights using the
back-propagation algorithm. Networks are trained based on samples generated
from the HK distribution. Obtained results demonstrate that the proposed method
can be used to accurately estimate the HK distribution parameters.
