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Volume 8, Issue 3
Image Denoising via Residual Kurtosis Minimization

Tristan A. Hearn & Lothar Reichel

DOI: 10.4208/nmtma.2015.m1337

Numer. Math. Theor. Meth. Appl., 8 (2015), pp. 406-424.

Published online: 2015-08

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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.

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@Article{NMTMA-8-406, author = {Tristan A. Hearn and Lothar Reichel}, title = {Image Denoising via Residual Kurtosis Minimization}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2015}, volume = {8}, number = {3}, pages = {406--424}, 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.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2015.m1337}, url = {http://global-sci.org/intro/article_detail/nmtma/12416.html} }
TY - JOUR T1 - Image Denoising via Residual Kurtosis Minimization AU - Tristan A. Hearn & Lothar Reichel JO - Numerical Mathematics: Theory, Methods and Applications VL - 3 SP - 406 EP - 424 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.4208/nmtma.2015.m1337 UR - https://global-sci.org/intro/article_detail/nmtma/12416.html KW - AB -

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.

Tristan A. Hearn and Lothar Reichel. (2015). Image Denoising via Residual Kurtosis Minimization. Numerical Mathematics: Theory, Methods and Applications. 8 (3). 406-424. doi:10.4208/nmtma.2015.m1337
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