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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.
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 -
