DocumentCode :
2733638
Title :
Image restoration using Lagrange programming neural networks
Author :
Zhu, Xinen ; Zhang, Shaoting ; Constantinides, A.G.
Author_Institution :
Dept. of Electr. Eng., Imperial Coll., London
fYear :
1991
fDate :
8-14 Jul 1991
Abstract :
Summary form only given, as follows. An approach involving the use of Lagrange programming neural networks to restore degraded images is proposed. Three kinds of neurons take part in the restoration process: parameter neurons, variable neurons, and Lagrange neurons. The parameter neurons adapt to various input parameters, while the variable and Lagrange neurons construct a canonical computational circuit to fulfill fundamental solution-finding computation. Maximum entropy restoration is completed with ease by taking advantage of the features of robust stability and functional flexibility of the neural network. High-quality images were obtained in experiments
Keywords :
computerised picture processing; neural nets; Lagrange neurons; Lagrange programming neural networks; degraded images; functional flexibility; image restoration; input parameters; maximum entropy restoration; parameter neurons; robust stability; solution-finding computation; variable neurons; Circuits; Degradation; Educational institutions; Entropy; Image restoration; Lagrangian functions; Neural networks; Neurons; Robust stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
Type :
conf
DOI :
10.1109/IJCNN.1991.155516
Filename :
155516
Link To Document :
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