Fundamentals of digital image processing
Fundamentals of digital image processing
Digital image processing: concepts, algorithms and scientific applications
Digital image processing: concepts, algorithms and scientific applications
Applied Neural Networks For Signal Processing
Applied Neural Networks For Signal Processing
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
Vector-entropy optimization-based neural-network approach to image reconstruction from projections
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A neural network optimization-based method of image reconstruction from projections
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing in advanced nondestructive materials inspection
An analytical approach to the image reconstruction problem using EM algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A neuronal approach to the statistical image reconstruction from projections problem
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Objective: In this paper a new approach to tomographic image reconstruction from projections is developed and investigated. Method: To solve the reconstruction problem a special neural network which resembles a Hopfield net is proposed. The reconstruction process is performed during the minimizing of the energy function in this network. To improve the performance of the reconstruction process an entropy term is incorporated into energy expression. Result and conclusion: The approach presented in this paper significantly decreases the complexity of the reconstruction problem.