Title :
Fast fixed-point neural blind-deconvolution algorithm
Author_Institution :
Fac. of Eng., Perugia Univ., Terni, Italy
fDate :
3/1/2004 12:00:00 AM
Abstract :
The aim of this letter is to introduce a new blind-deconvolution algorithm based on fixed-point optimization of a "Bussgang"-type cost function. The cost function relies on approximate Bayesian estimation achieved by an adaptive neuron. The main feature of the presented algorithm is fast convergence that guarantees good deconvolution performances with limited computational demand as compared with algorithms of the same class.
Keywords :
belief networks; blind equalisers; convergence; deconvolution; neural nets; optimisation; Bussgang-type cost function; adaptive neuron; fast convergence; fixed-point optimization; neural Bayesian estimation; neural blind deconvolution algorithm; Bayesian methods; Convergence; Cost function; Deconvolution; Distortion; Finite impulse response filter; Geophysical measurements; Image storage; Neurons; Signal processing; Algorithms; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.824258
