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