Authors:
Catalina Cocianu
1
;
Luminita State
2
;
Vlamos Panayiotis
3
and
Viorica Stefanescu
1
Affiliations:
1
Academy of Economic Studies, Romania
;
2
University of Pitesti, Romania
;
3
Hellenic Open University, Greece
Keyword(s):
Noise removal, image processing, regression, filtering, multiresolution analysis, wavelet transform, statistical image restoration techniques, least mean squares techniques
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Signal Reconstruction
Abstract:
The investigated noise removal algorithms are HRBA, HSBA, HBA, AMVR, PNRA, MMSE, MNR, MNR2 and NFPCA. The multiresolution support provides a suitable framework for noise filtering and for restoration purposes by noise suppression. The techniques used in the paper are mainly based on the statistically significant wavelet coefficients specifying the support. The performed tests reveal that the use of the multiresolution support proves powerful and offers a versatile way to handle noise of different classes of distributions.