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

• Published in: Informatica Economica Vol. XI; no. 1; pp. 73 - 78
• Main Authors: Panayiotis, VLAMOS, Luminita, STATE, Catalina, COCIANU
• Format: Journal Article
• Language: English
• Published: Academy of Economic Studies - Bucharest, Romania 2007; Inforec Association
• Series: Informatica Economica
• Subjects: feature extraction; Generalized Hebbian Algorithm; image restoration; multiresolution support set; PCA; wavelet transform
• ISSN: 1453-1305

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.