Share Email Print
cover

Proceedings Paper

Application of neural network to restoration of signals degraded by a stochastic, shift-variant impulse response function and additive noise
Author(s): Mehmet Bilgen; Hsien-Sen Hung
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

An artificial neural network is adopted for estimating discrete (sampled in time and quantized in amplitude) signals degraded by a stochastic, shift-variant impulse response (blur) function in the presence of noise. The signal restoration problem is formulated as a combinatorial optimization problem wherein a nonlinear cost function, termed stochastic constrained restoration error energy, is to be minimized. By matching the cost function with the energy function of the associated neural network, the interconnection strengths and bias inputs of the neural network are related to the degraded signal, blur statistics, and constraint parameters. The solution which minimizes the energy function of the neural network is thus obtained iteratively by the simulated annealing algorithm. Simulation results show the effectiveness of the proposed algorithm which has, in addition, the capability of imposing level constraints on the original signal.

Paper Details

Date Published: 1 October 1991
PDF: 9 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48384
Show Author Affiliations
Mehmet Bilgen, Iowa State Univ. (United States)
Hsien-Sen Hung, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top