Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments

TL;DR

This paper introduces a neural network approach that estimates parameters of the homodyned K distribution using trainable fractional-order moments, improving accuracy over previous methods in modeling scattering phenomena.

Contribution

The novel aspect is making the fractional-order moments trainable variables optimized via back-propagation, enhancing parameter estimation accuracy for the HK distribution.

Findings

Accurately estimates HK distribution parameters.

Outperforms previous estimation methods.

Uses trainable fractional-order moments for improved modeling.

Abstract

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.