Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study

Fateme Taherkhani; Mohammad Jalal Rastegar Fatemi; Yousef Ganjdanesh

doi:doi:10.11648/j.cssp.20150406.11

| Peer-Reviewed

Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study

Fateme Taherkhani, Mohammad Jalal Rastegar Fatemi, Yousef Ganjdanesh

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 4, Issue 6)

Received: 17 November 2015 Accepted: 23 December 2015 Published: 15 January 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Combined techniques consisting of traditional linear filtering and total variation de-noising are studied to de-noise images polluted with composite noises from different sources. Series and parallel approaches are compared theoretically. Some further discussions are given about parameters and their effect on the optimization problem. A simultaneous method is proposed and simulated using the convex optimization toolbox, comparing different alternative methods with respect to signal to noise ratio and error covariance matrix. The proposed method could be used to enhance the smoothing performance.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 4, Issue 6)
DOI	10.11648/j.cssp.20150406.11
Page(s)	55-59
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Image Processing, Total Variation De-noising, Total Variation Smoothing, Linear Filter, Combined Linear Filtering and Total Variation De-noising

References

[1]	L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithm”. Physica D, 1992, 60:259–268.
[2]	A. Chambolle, “An algorithm for total variation minimization and applications”, J. of Math. Imagingand Vision, 2004, 20:89–97.
[3]	A. Chambolle and P.L. Lions, “Image recovery via total variation minimization and related problems”, Numerische Mathematik, 1997, 76:167–188.
[4]	T. F. Chan, S. Osher, and J. Shen, “The digital TV ﬁlter and nonlinear denoising”, IEEE Trans. Image, Process.2001, 10(2):231–241.
[5]	Selesnick, IW.; Graber, H.L.; Pfeil, D.S.; Barbour, R.L., "Simultaneous Low-Pass Filtering and Total Variation Denoising," IEEE Transactions on Signal Processing,2014, vol.62, no.5, pp.1109,1124, March1.
[6]	Hu, Y. and Jacob, M., “Higher degree total variation (HDTV) regularization for image recovery”, IEEE Trans. Image Processing, 2012, 21:2559–257.
[7]	L. Condat, “A direct algorithm for 1D total variation denoising” Technical report, Hal-00675043, 2012.http://hal.archives-ouvertes.fr/.
[8]	J. Bect, L. Blanc-F´eraud, G. Aubert, and A. Chambolle, “A l1-uniﬁed variational framework for image restoration”, Lecture Notes in Computer Sciences, 2004, volume 3024, pages 1–134.
[9]	K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation”, SIAM J. Imag. Sci., 2010, 3(3):492–526.
[10]	Alter, F., Durand, S., Froment, J., “Adapted total variation for artifact free decompression of JPEG images”, J. Math. Imaging Vis. 2005, 23, 199–211.
[11]	Zhu, M., Wright, S., Chan, T.F., “Duality-based algorithms for total-variation-regularized image restoration”, Comput. Optim. Appl., 2008, 22, 161–176.
[12]	Krishnan, D., Ping, L., Yip, A.M., “A primal-dual active-set methods for non-negativity constrained total variation deblurring problems”, IEEE Trans. Image Process. 2007, 16, 2766–2777.
[13]	Darbon, J., Sigelle, M. “Image restoration with discrete constrained total variation: Part I: fast and exact optimization”, J. Math. Imaging Vis. 2006, 26, 261–276.
[14]	Goldfarb, D., Yin, W., “Second-order cone programming methods for total variation-based image restoration”, SIAM J. Sci. Comput.2005, 27, 622–645.
[15]	Grant, Michael, Stephen Boyd, and Yinyu Ye. "CVX: Matlab software for disciplined convex programming." (2008).
[16]	Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
[17]	Parker, Jim R. Algorithms for image processing and computer vision. John Wiley & Sons, 2010.
[18]	Petrou, Maria, and Costas Petrou. Image processing: the fundamentals. John Wiley & Sons, 2010.
[19]	Bovik, Alan C. Handbook of image and video processing. Academic Press, 2010.
[20]	Buades, Antoni, Bartomeu Coll, and Jean-Michel Morel. "A review of image denoising algorithms, with a new one." Multiscale Modeling & Simulation 4.2 (2005): 490-530.
[21]	Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis, and machine vision. Cengage Learning, 2014.
[22]	Dabov, Kostadin, et al. "Image denoising by sparse 3-D transform-domain collaborative filtering." Image Processing, IEEE Transactions on 16.8 (2007): 2080-2095.
[23]	Kojima, Shinya, et al. "Iterative image reconstruction that includes a total variation regularization for radial MRI." Radiological physics and technology (2015): 1-10.‏
[24]	Liao, Chien-Sheng, et al. "Denoising stimulated Raman spectroscopic images by total variation minimization." The Journal of Physical Chemistry C 119.33 (2015): 19397-19403.‏
[25]	Boţ, RaduIoan, and Christopher Hendrich. "Convergence analysis for a primal-dual monotone+ skew splitting algorithm with applications to total variation minimization." Journal of mathematical imaging and vision 49.3 (2014): 551-568.‏
[26]	Liu, Xinwu, and Lihong Huang. "A new nonlocal total variation regularization algorithm for image denoising." Mathematics and Computers in Simulation 97 (2014): 224-233.‏
[27]	Rose, Sean, et al. "Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization." Medical physics 42.5 (2015): 2690-2698.‏
[28]	Langer, Andreas. "Automated Parameter Selection for Total Variation Minimization in Image Restoration." arXiv preprint arXiv: 1509.07442 (2015).

Cite This Article

Plain Text BibTeX RIS

APA Style

Fateme Taherkhani, Mohammad Jalal Rastegar Fatemi, Yousef Ganjdanesh. (2016). Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Science Journal of Circuits, Systems and Signal Processing, 4(6), 55-59. https://doi.org/10.11648/j.cssp.20150406.11

Copy | Download

ACS Style

Fateme Taherkhani; Mohammad Jalal Rastegar Fatemi; Yousef Ganjdanesh. Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Sci. J. Circuits Syst. Signal Process. 2016, 4(6), 55-59. doi: 10.11648/j.cssp.20150406.11

Copy | Download

AMA Style

Fateme Taherkhani, Mohammad Jalal Rastegar Fatemi, Yousef Ganjdanesh. Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Sci J Circuits Syst Signal Process. 2016;4(6):55-59. doi: 10.11648/j.cssp.20150406.11

Copy | Download

@article{10.11648/j.cssp.20150406.11,
  author = {Fateme Taherkhani and Mohammad Jalal Rastegar Fatemi and Yousef Ganjdanesh},
  title = {Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {4},
  number = {6},
  pages = {55-59},
  doi = {10.11648/j.cssp.20150406.11},
  url = {https://doi.org/10.11648/j.cssp.20150406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20150406.11},
  abstract = {Combined techniques consisting of traditional linear filtering and total variation de-noising are studied to de-noise images polluted with composite noises from different sources. Series and parallel approaches are compared theoretically. Some further discussions are given about parameters and their effect on the optimization problem. A simultaneous method is proposed and simulated using the convex optimization toolbox, comparing different alternative methods with respect to signal to noise ratio and error covariance matrix. The proposed method could be used to enhance the smoothing performance.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study
AU  - Fateme Taherkhani
AU  - Mohammad Jalal Rastegar Fatemi
AU  - Yousef Ganjdanesh
Y1  - 2016/01/15
PY  - 2016
N1  - https://doi.org/10.11648/j.cssp.20150406.11
DO  - 10.11648/j.cssp.20150406.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 55
EP  - 59
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20150406.11
AB  - Combined techniques consisting of traditional linear filtering and total variation de-noising are studied to de-noise images polluted with composite noises from different sources. Series and parallel approaches are compared theoretically. Some further discussions are given about parameters and their effect on the optimization problem. A simultaneous method is proposed and simulated using the convex optimization toolbox, comparing different alternative methods with respect to signal to noise ratio and error covariance matrix. The proposed method could be used to enhance the smoothing performance.
VL  - 4
IS  - 6
ER  -

Copy | Download

Author Information

Fateme Taherkhani

Department of Electrical Engineering, Colledge of Technical and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
Mohammad Jalal Rastegar Fatemi

Department of Electrical Engineering, Colledge of Technical and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
Yousef Ganjdanesh

Department of Electrical Engineering, Colledge of Technical and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Fateme Taherkhani, Mohammad Jalal Rastegar Fatemi, Yousef Ganjdanesh. (2016). Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Science Journal of Circuits, Systems and Signal Processing, 4(6), 55-59. https://doi.org/10.11648/j.cssp.20150406.11

Copy | Download

ACS Style

Fateme Taherkhani; Mohammad Jalal Rastegar Fatemi; Yousef Ganjdanesh. Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Sci. J. Circuits Syst. Signal Process. 2016, 4(6), 55-59. doi: 10.11648/j.cssp.20150406.11

Copy | Download

AMA Style

Fateme Taherkhani, Mohammad Jalal Rastegar Fatemi, Yousef Ganjdanesh. Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study. Sci J Circuits Syst Signal Process. 2016;4(6):55-59. doi: 10.11648/j.cssp.20150406.11

Copy | Download

@article{10.11648/j.cssp.20150406.11,
  author = {Fateme Taherkhani and Mohammad Jalal Rastegar Fatemi and Yousef Ganjdanesh},
  title = {Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {4},
  number = {6},
  pages = {55-59},
  doi = {10.11648/j.cssp.20150406.11},
  url = {https://doi.org/10.11648/j.cssp.20150406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20150406.11},
  abstract = {Combined techniques consisting of traditional linear filtering and total variation de-noising are studied to de-noise images polluted with composite noises from different sources. Series and parallel approaches are compared theoretically. Some further discussions are given about parameters and their effect on the optimization problem. A simultaneous method is proposed and simulated using the convex optimization toolbox, comparing different alternative methods with respect to signal to noise ratio and error covariance matrix. The proposed method could be used to enhance the smoothing performance.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Linear Filtering and Total Variation De-noising of Composite Noises: Different Architectures and Simulation Study
AU  - Fateme Taherkhani
AU  - Mohammad Jalal Rastegar Fatemi
AU  - Yousef Ganjdanesh
Y1  - 2016/01/15
PY  - 2016
N1  - https://doi.org/10.11648/j.cssp.20150406.11
DO  - 10.11648/j.cssp.20150406.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 55
EP  - 59
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20150406.11
AB  - Combined techniques consisting of traditional linear filtering and total variation de-noising are studied to de-noise images polluted with composite noises from different sources. Series and parallel approaches are compared theoretically. Some further discussions are given about parameters and their effect on the optimization problem. A simultaneous method is proposed and simulated using the convex optimization toolbox, comparing different alternative methods with respect to signal to noise ratio and error covariance matrix. The proposed method could be used to enhance the smoothing performance.
VL  - 4
IS  - 6
ER  -

Copy | Download