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.

翻译：智者K(HK)的分布被广泛用于描述在超声成像或光学等不同研究领域产生的散射现象。 在这项工作中,我们提出一种基于机器的学习方法来估计香港的分布参数。我们开发了神经网络,可以根据信号对噪音比率、扭曲和通过分序时间计算得出的香港分布参数来估计。与以前的方法相比,我们认为,这些时刻的顺序是可训练的变量,可以与网络重量一起使用反向调整算法加以优化。网络是根据香港分布的样本来训练的。获得的结果表明,可以使用拟议的方法准确估计香港的分布参数。