Dynamic image modelling by neural networks
作者:
SATYENDRA BHAMA,
HARPREET SINGH,
DEVINDER KAUR,
期刊:
International Journal of Systems Science
(Taylor Available online 1994)
卷期:
Volume 25,
issue 4
页码: 803-811
ISSN:0020-7721
年代: 1994
DOI:10.1080/00207729408928997
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Sometimes an image can be characterized with the help of a dynamic system in terms of a first-order differential equation, e.g. when a scanner output contains additive noise and/or is degraded due to interaction between the sensing elements (the cameras) or by other phenomena. In this paper, an algorithm for image estimation from the noisy output of a scanner is hypothesized. Determination of the parametersA, B, and theCmatrices of a dynamic system leads to the design of an estimator whose input is the output of a scanner with uniform speed. A portion of the overall algorithm using neural networks trained by a gradient descent learning algorithm is discussed in detail. In particular, the determination ofA. B, Cfrom states, time derivatives of states and inputs are highlighted. The details of implementation are included. The salient points regarding the development of the complete algorithm are discussed. It is hoped that the results achieved so far will give rise to new techniques for the application of neural networks to image enhancement in particular and to image processing in general.
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