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Image Denoising via Residual Kurtosis Minimization

Part of: Numerical methods in Fourier analysis Statistics Numerical linear algebra

Published online by Cambridge University Press:  05 August 2015

Tristan A. Hearn  and
Lothar Reichel
Tristan A. Hearn
Affiliation:
Department of Mathematical Sciences, Kent State University, Kent, OH 44242, USA
Lothar Reichel*
Affiliation:
Department of Mathematical Sciences, Kent State University, Kent, OH 44242, USA
*
*Email address: thearn@math.kent.edu (T. A. Hearn) and reichel@math.kent.edu (L. Reichel)
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Abstract

A new algorithm for the removal of additive uncorrelated Gaussian noise from a digital image is presented. The algorithm is based on a data driven methodology for the adaptive thresholding of wavelet coefficients. This methodology is derived from higher order statistics of the residual image, and requires no a priori estimate of the level of noise contamination of an image.

Keywords

Denoisingnoise estimationkurtosis minimizationwavelet shrinkage

MSC classification

Secondary: 65F22: Ill-posedness, regularization 62-07: Data analysis 65T60: Wavelets
Type
Research Article
Information
Numerical Mathematics: Theory, Methods and Applications , Volume 8 , Issue 3 , August 2015 , pp. 406 - 424
Copyright
Copyright © Global-Science Press 2015 

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