@Article{NMTMA-8-3,
author = {},
title = {Image Denoising via Residual Kurtosis Minimization},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2015},
volume = {8},
number = {3},
pages = {406--424},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2015.m1337},
url = {https://global-sci.com/article/90580/image-denoising-via-residual-kurtosis-minimization}
}