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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Image Denoising Based on MORF and Minimization Total Variation
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Chengwu Lu, "Image Denoising Based on MORF and Minimization Total Variation," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 2, pp. 792-796, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.299,
author = {Chengwu Lu},
title = {Image Denoising Based on MORF and Minimization Total Variation},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {2},
year = {2007},
isbn = {0-7695-2909-7},
pages = {792-796},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.299},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Image Denoising Based on MORF and Minimization Total Variation
SN - 0-7695-2909-7
SP792
EP796
A1 - Chengwu Lu,
PY - 2007
VL - 2
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Chengwu Lu, Chongqing University of arts and sciences, China; Xidian University, China
A new tight frame called monoscale orthonormal ridgelet frame (MORF) is presented in this paper. The localization principle and the orthonormal ridgelet constructed by Donoho are applied to construct the MORF. And then, It is deployed for image denoising, where a thresholding process for MORF coefficients of the noisy image is carried out firstly. To remove the artifact such as pseudo-Gibbs and to restore sharp discontinuities, while the other structures are preserved, a minimization total variation approach is applied to restore the denoised results. The experiments show that the method has more improvement both in terms of PSNR and visual effect than traditional thresholding method for image denoising.
Citation:
Chengwu Lu, "Image Denoising Based on MORF and Minimization Total Variation," snpd, vol. 2, pp.792-796, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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