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AASCIT Communications | Volume 1, Issue 1 | Jul. 8, 2014 online | Page:4-5
Multiscale Analysis Wavelet Coefficients with Smoothness-Constrained Mean Noisy Configuration Algorithm
Abstract
Wavelet theory has played a particularly important role in multiscale analysis due to the fact that the basis functions are well suited to analyze local scale phenomena. This research also endows wavelets with a remarkable property for denoising in a wavelet based framework. In this paper, we show that effective noise uppression may be achieved by wavelet shrinkage.
Authors
[1]
Reza khajehmohammadlou, Department of Electrical Engineering, Miandoab Branch, Islamic Azad University, Miandoab, Iran.
Keywords
Algorithm, Image, Analysis, Pattern
Reference
[1]
D. Donohoand I. Johnstone,“Adapting to unknownsmoothnessvia waveletshrinkage,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1224, 2005.
[2]
Sedghi T., “Non Linear Transform for Retrieval System in Consideration of Feature CombinationTechnique” International Journal of Natural and Engineering Sciences, Vol 6 No 3, 2012.
[3]
H. Krim and I. C. Schick,“Minimax description length for signal denoising andoptimized representation,” IEEE Trans. Information Theory, vol. 45, no. 3, pp. 898–908, 2009.
[4]
M. Neumann and R. von Sachs and,Wavelet thresholding:Beyond the gaussianiidsituation, vol. 103 of Wavelets and Statistics, Lecture Notes in Statistics, Springer-Verlag, Nov. 2004.
[5]
Sedghi T., “A Fast and Effective Model for cyclic Analysis and its application in classification” Arabian Journal for Science and Engineering Vol. 38 October 2012.
Arcticle History
Submitted: Jun. 3, 2014
Accepted: Jun. 28, 2014
Published: Jul. 8, 2014
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