The Use of Features Extracted from Noisy Samples for Image Restoration Purposes

An important feature of neural networks is the ability they have to learn from their environment, and, through learning to improve performance in some sense. In the following we restrict the development to the problem of feature extracting unsupervised neural networks derived on the base of the biol...

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Bibliographic Details
Published inInformatica Economica Vol. XI; no. 1; pp. 73 - 78
Main Authors Panayiotis, VLAMOS, Luminita, STATE, Catalina, COCIANU
Format Journal Article
LanguageEnglish
Published Academy of Economic Studies - Bucharest, Romania 2007
Inforec Association
SeriesInformatica Economica
Subjects
feature extraction
Generalized Hebbian Algorithm
image restoration
multiresolution support set
PCA
wavelet transform
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Summary:An important feature of neural networks is the ability they have to learn from their environment, and, through learning to improve performance in some sense. In the following we restrict the development to the problem of feature extracting unsupervised neural networks derived on the base of the biologically motivated Hebbian self-organizing principle which is conjectured to govern the natural neural assemblies and the classical principal component analysis (PCA) method used by statisticians for almost a century for multivariate data analysis and feature extraction. The research work reported in the paper aims to propose a new image reconstruction method based on the features extracted from the noise given by the principal components of the noise covariance matrix.
ISSN:1453-1305
1842-8088