Paper Info
Title
Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images.
Abstract
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.
Year
DOI
Venue
2018
10.1007/s11042-017-5592-y
Multimedia Tools Appl.
Keywords
Field
DocType
Poisson noise, X-ray image, Adaptive filter, Artificial neural network, Optimization, Friedman’s test
Noise reduction,Least mean squares filter,Particle swarm optimization,Peak signal-to-noise ratio,Pattern recognition,Computer science,Multilayer perceptron,Adaptive filter,Artificial intelligence,Artificial neural network,Shot noise
Journal
Volume
Issue
ISSN
77
18
1380-7501
Citations 
PageRank 
References 
1
0.34
21
Authors
2
Name
Order
Citations
PageRank
manish kumar140169.07
Sudhansu Kumar Mishra2394.69