Mixed noise suppression from color Doppler ultrasound (CDUS) images is always a challenging task because the noise distribution usually does not have a parametric model and heavy tail. It affects the inherent features of the image awkwardly. Consequently, identifying an internal blockage or hemorrhage of the patient becomes arduous in such conditions. An acquired CDUS image is majorly affected by speckle noise and can be coupled with Gaussian and impulse noises. In this paper, the evolutionary multichannel Jaya based functional link artificial neural network (named as M-Jaya-FLANN) has been proposed to get rid of mixed noise from the CDUS images. The subjective evaluation and the measurement of qualitative metrics, such as structural similarity index, computational time, convergence rate, and Friedman’s test are carried out for the performance analysis of different filters. The research outcomes exhibit the supremacy of the proposed filter over other competitive filters and can handle real-time noise elimination after completion of training. For the experimentation purpose, CDUS image data are collected from Medanta hospital, Ranchi, India.

彩色多普勒超声（CDUS）图像中的混合噪声抑制始终是一项艰巨的任务，因为噪声分布通常没有参数模型和粗尾。它笨拙地影响图像的固有特征。因此，在这种情况下识别患者的内部阻塞或出血变得困难。采集的CDUS图像主要受斑点噪声的影响，并且可能与高斯和脉冲噪声耦合。本文提出了一种基于演化多通道Jaya的功能链接人工神经网络（称为M-Jaya-FLANN），以消除CDUS图像中的混合噪声。主观评估和定性指标的度量，例如结构相似性指标，计算时间，收敛速度，进行弗雷德曼（Friedman）测试，以分析不同滤波器的性能。研究成果显示了所提出的滤波器优于其他竞争滤波器的优势，并且可以在训练完成后处理实时噪声消除。出于实验目的，从印度兰契市Medanta医院收集了CDUS图像数据。