An optimality principle for unsupervised learning
Advances in neural information processing systems 1
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On a Certain Class of Algorithms for Noise Removal in Image Processing: A Comparative Study
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
On relative convergence properties of principal component analysis algorithms
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Benchmark database and GUI environment for printed Arabic text recognition research
WSEAS Transactions on Information Science and Applications
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Image restoration methods are used to improve the appearance of an image by application of a restoration process that uses a mathematical model for image degradation. The restoration can be viewed as a process that attempts to reconstruct or recover an image that has been degraded by using some a priori knowledge about the degradation phenomenon. Principal component analysis allows the identification of a linear transformation such that the axes of the resulted coordinate system correspond to the largest variability of the investigated signal. The advantages of using principal components reside from the fact that bands are uncorrelated and no information contained in one band can be predicted by the knowledge of the other bands, therefore the information contained by each band is maximum for the whole set of bits. The multiresolution support set is a data structure suitable for developing noise removal algorithms. The multiresolution algorithms perform the restoration tasks by combining, at each resolution level, according to a certain rule, the pixels of a binary support image. The multiresolution support can be computed using the statistically significant wavelet coefficients. We investigate the comparative performance of different PCA algorithms derived from Hebbian learning, lateral interaction algorithms and gradient-based learning for digital signal compression and image processing purposes. The final sections of the paper focus on PCA based approaches for image restoration tasks based on the multirezolution support set as well as on PCA based shrinkage technique for noise removal. The proposed algorithms were tested and some of the results are presented and commented in the final part of each section.