文章基本信息

标题：The Use of Features Extracted from Noisy Samples for Image Restoration Purposes
本地全文：下载
作者：State, Luminita ; Cocianu, Catalina ; Vlamos, Panayiotis 等
期刊名称：Informatica Economica
印刷版ISSN：1453-1305
出版年度：2007
卷号：XI
期号：1
页码：73-78
出版社：Academy of Economic Studies - Bucharest, Romania
摘要：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.
关键词：feature extraction; PCA; Generalized Hebbian Algorithm; image restoration; wavelet transform; multiresolution support set