ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

present work were compared given the difference images obtained by subtracting the
original image to the noisy images. Based on the results obtained, all methodologies
have similar denoising capabilities, however the best methodology given the average
computation time, noise removal and edge preservation capabilities is the one proposed
by Xu et al.. It has also the advantage of not being developed for a specific type of noise
(AWGN), and thus may be more useful in a broader spectrum of practical situations.
This methodology has the main disadvantages of smoothing intermediate transitions,
and is also highly dependent on the noise power estimation in each decomposition scale.
ACKNOWLEDGMENTS
The authors would like to acknowledge the support given by the Portuguese Foundation
for Science and Technology (FCT) through the PhD grant SFRH/BDE/51143/2010.
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