title: Blur identification by multilayer neural network based on multivalued neurons.
abstract: A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.
authors.affiliation: Texas A&M University-Texarkana, Texarkana, TX 75505 USA. igor.aizenberg@tamut.edu
authors.forename: Igor
authors.initials: I
authors.lastname: Aizenberg
authors.forename: Dmitriy V
authors.initials: DV
authors.lastname: Paliy
authors.forename: Jacek M
authors.initials: JM
authors.lastname: Zurada
authors.forename: Jaakko T
authors.initials: JT
authors.lastname: Astola
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005246:Feedback; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution