A Novel Noise Removal Method Using Neural Networks

Catalina Lucia Cocianu () and Alexandru Daniel Stan ()

Informatica Economica, 2016, vol. 20, issue 3, 66-75

Abstract: In this paper is presented a new technique consisting in applying some pre-whitening and shrinkage methods followed by a neural network-based supervised approach for correlated noise removal purposed. In our work the type of noise and the covariance matrix of noise are known or can be estimated using the “white wall†method. Due to data dimensionality, a PCA-based compression technique is used to obtain a tractable solution. The local memories of the neurons are determined using a supervised learning process based on the compressed pre-processed inputs and the compressed version of the original images. The proposed method is evaluated using some of the most commonly used indicators and the results are reported in the third section of the paper. The conclusive remarks together with suggestions for further work are supplied in the final part of the paper.

Keywords: Digital Image Processing; Correlated Noise Removal; Supervised Learning; Neural Networks; Shrinkage Techniques (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://revistaie.ase.ro/content/79/07%20-%20Cocianu,%20Stan.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:20:y:2016:i:3:p:66-75

Access Statistics for this article

Informatica Economica is currently edited by Ion Ivan

More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Paul Pocatilu ().