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Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images

机译：基于Jaya的功能链接多层感知器自适应滤波器，用于抑制X射线图像的泊松噪声

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摘要

In this paper, a parameterless Jaya optimization based neural network filter named as Jaya-functional link multilayer perceptron (Jaya-FLMLP) is proposed for the elimination of Poisson noise from X-ray images. In this proposed adaptive filter, Jaya is applied for updating the weights of the FLMLP network. The proposed neural filter is a combination of a functional link artificial neural network (FLANN) and Multilayer Perceptron (MLP) network. The performance of Jaya-FLMLP is also compared with other five competitive networks such as Wiener, MLP, Least Mean Squares based Functional Link Artificial Neural Network (LMS-FLANN), Particle Swarm Optimization based Functional Link Artificial Neural Network (PSO-FLANN) and Cat Swarm Optimization based Functional Link Artificial Neural Network (CSO-FLANN). The comparison of performance is investigated by the Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Reduction in Decibels (NRDB) values. The simulation results and non-parametric Friedman's test reveal the superiority of the Jaya-FLMLP filter over others.
机译：本文提出了一种基于无参数Jaya优化的神经网络滤波器，称为Jaya功能链接多层感知器（Jaya-FLMLP），用于消除X射线图像中的泊松噪声。在提出的自适应滤波器中，Jaya被用于更新FLMLP网络的权重。所提出的神经过滤器是功能链接人工神经网络（FLANN）和多层感知器（MLP）网络的组合。 Jaya-FLMLP的性能也与其他五个竞争性网络进行了比较，例如维纳，MLP，基于最小均方的功能链接人工神经网络（LMS-FLANN），基于粒子群优化的功能链接人工神经网络（PSO-FLANN）和基于Cat群优化的功能链接人工神经网络（CSO-FLANN）。通过结构相似性指数（SSIM），峰值信噪比（PSNR）和分贝降低噪声（NRDB）值来研究性能的比较。仿真结果和非参数弗里德曼测试表明，Jaya-FLMLP滤波器优于其他滤波器。

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