A Medical Image Denoising Method using Subband Adaptive Thresholding Based on a Shearlet Transform

Petrov, Miroslav. "A Medical Image Denoising Method using Subband Adaptive Thresholding Based on a Shearlet Transform." Serdica Journal of Computing 10.3-4 (2016): 219-230. <http://eudml.org/doc/289517>.

@article{Petrov2016,
abstract = {The image denoising process is of great importance when analyzing images and their visualization. A major problem is finding the boundary between clearing the noise and keeping the salient features in the images. This paper proposes adaptive subband threshold image denoising in a shearlet domain based on the Shannon entropy. The method does not suppose a specific type of noise, it does not require data for its spectrum, nor does it lead to highly complex computational algorithms. ACM Computing Classification System (1998): I.5.4, I.4.3, I.4.5.},
author = {Petrov, Miroslav},
journal = {Serdica Journal of Computing},
keywords = {Medical Image; Denoising; Shearlet Tresholding; Shannon Entropy; Rician Noise},
language = {eng},
number = {3-4},
pages = {219-230},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {A Medical Image Denoising Method using Subband Adaptive Thresholding Based on a Shearlet Transform},
url = {http://eudml.org/doc/289517},
volume = {10},
year = {2016},
}

TY - JOUR
AU - Petrov, Miroslav
TI - A Medical Image Denoising Method using Subband Adaptive Thresholding Based on a Shearlet Transform
JO - Serdica Journal of Computing
PY - 2016
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 10
IS - 3-4
SP - 219
EP - 230
AB - The image denoising process is of great importance when analyzing images and their visualization. A major problem is finding the boundary between clearing the noise and keeping the salient features in the images. This paper proposes adaptive subband threshold image denoising in a shearlet domain based on the Shannon entropy. The method does not suppose a specific type of noise, it does not require data for its spectrum, nor does it lead to highly complex computational algorithms. ACM Computing Classification System (1998): I.5.4, I.4.3, I.4.5.
LA - eng
KW - Medical Image; Denoising; Shearlet Tresholding; Shannon Entropy; Rician Noise
UR - http://eudml.org/doc/289517
ER -