SEARCH EFFICIENCY FUNCTION-PSO COMBINATION FORBLIND IMAGE RESTORATION
Résumé
In this work we propose, for the first time, a combined method for blind image restoration. This combination is based on PSO
and the integration in it of the search efficiency function that represent the optimal searching strategy elaborated by honeybees
when searching for food, and its use for the first time in image processing, to carry out the restoration operation of the only
blurred images and blurred and noisy ones. The results we got were excellent.
Références
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Multiscale Maximum Entropy Image Restoration
Algorithm”, Proceedings of the First International
Conference on Innovative Computing, Information and
Control (ICICIC'06), IEEE, 2006.
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International Conference on Wavelet Analysis and
Pattern Recognition, Beijing, China, 2007.
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Image Restoration”, IEEE Transactions On Neural
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Hongdong Li, “On Non-blind Image Restoration”,
IEEE, 2009.
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Katsaggelos, “Parameter Estimation in TV Image
Restoration Using Variational Distribution
Approximation”, IEEE Transactions On Image
Processing, Vol. 17, NO. 3, March 2008.
[20] Shuai Lou, Zhenliang Ding, Feng Yuan, Jing Li,
“Image Restoration Based on Wavelet-Domain
Contextual Hidden Markov Tree Model”, International
Conference on Computer Science and Software
Engineering, IEEE, 2008.
[21] Wang Hong-Zhi Zhao Shuang Lv Hong-Wu, “SuperResolution Image Restoration with L-Curve”, IEEE
Congress on Image and Signal Processing, 2008.
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“Iterative Blind Image Motion Deblurring Via
Learning A No-Reference Image Quality Measure”,
ICIP 2007.
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HUANG, “Wavelet domain image restoration and
parameters estimation based on variational Bayesian
method and Student-t priors”, IEEE International
Symposium on Computational Intelligence and
Design, 2008.
[24] Youshen Xia, and Mohamed S. Kamel, “Novel
Cooperative Neural Fusion Algorithms for Image
Restoration and Image Fusion”, IEEE Transactions On
Image Processing, Vol. 16, NO. 2, February 2007.
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Periodic Noise Reduction”, Evo Workshops,
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2008.
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Processing, Second Edition, Prentice Hall, 2002.
[2] Mallat S., A Wavelet Tour Of Signal Processing,
Second Edition, Academic Press, Elsevier (USA),
1999.
[3] Pratt W. K., Digital Image Processing: PIKS Inside,
Third Edition, John Wiley & Sons, Inc., 2001.
[4] Perry S. W., Adaptive Image Restoration: Perceptron
Based Neural Network Model and Algorithms, Ph.D.
Theses, School of Electrical and Information
Engineering University of Sidney, Australia, 1999.
A. TOUMI& al.
140
[5] Smith S. K., Digital Signal Processing, A Practical
Guide for Engineers and Scientists, Newnes, 2003.
[6] BI Xiao-jun, WANG Ting, “Adaptive Blind Image
Restoration Algorithm of Degraded Image”, Congress
on Image and Signal Processing, 2008 IEEE
[7] Feng-qing Qin, Jun Min, Hong-rong Guo, “A Blind
Image Restoration Method Based on PSF Estimation”,
World Congress on Software Engineering, 2009 IEEE
[8] Jianjun Zhang, Qin Wang, “A New Regularization
Method for Bi-Level Image Restoration”,
ICALIP2008, 2008 IEEE.
[9] Jinyoung Youn, Younguk Park, Jeongho Shin, and
Joonki Paik, “Spatially Adaptive Image Restoration
and Its FIR Implementation”, Second International
Conference on Future Generation Communication and
Networking Symposia, 2008 IEEE.
[10] Rajeev Srivastava, Harish Parthasarthyt, JRP Guptat
and D. Roy Choudharyl, “Image Restoration from
Motion Blurred Image using PDEs formalism”, IEEE
International Advance Computing Conference (IACC
2009) Patiala, India, 6-7 March 2009.
[11] Satyadhyan Chickerur, Aswatha Kumar M , “A Robust
Cluster Based Approach for Image Restoration”, IEEE
Congress on Image and Signal Processing, 2008.
[12] Sun Jing, Wu Lehua, Gui Qi, “An Improved
Multiscale Maximum Entropy Image Restoration
Algorithm”, Proceedings of the First International
Conference on Innovative Computing, Information and
Control (ICICIC'06), IEEE, 2006.
[13] Sungjun Yim, Jeongho Shin, Joonki Paik, “BlockBased Fast Image Restoration”, Proceedings of the
International Conference on Wavelet Analysis and
Pattern Recognition, Beijing, China, 2007.
[14] [14] Varlen Grabski, “Digital Image Restoration Based
on Pixel Simultaneous Detection Probabilities”, IEEE
Transactions On Nuclear Science, Vol. 56, No. 3, June
2009.
[15] Yoshinori Abe and Youji Iiguni, “Fast Computation
Of The High Resolution Image Restoration By Using
The Discrete Cosine Transform”, ICASSP, IEEE,
2007.
[16] Abd-Krim Seghouane, “Model Selection Criteria for
Image Restoration”, IEEE Transactions On Neural
Networks, Vol. 20, NO. 8, August 2009.
[17] J.K. Prasanna and A.N. Rajagopalan, “Image
restoration using the particle filter: handling noncausality”, IEE Proc.-Vis. Image Signal Process., Vol.
153, No. 5, October, IEEE, 2006.
[18] Pradeepa D. Samarasinghe, Rodney A. Kennedy,
Hongdong Li, “On Non-blind Image Restoration”,
IEEE, 2009.
[19] S. Derin Babacan, Rafael Molina, and Aggelos K.
Katsaggelos, “Parameter Estimation in TV Image
Restoration Using Variational Distribution
Approximation”, IEEE Transactions On Image
Processing, Vol. 17, NO. 3, March 2008.
[20] Shuai Lou, Zhenliang Ding, Feng Yuan, Jing Li,
“Image Restoration Based on Wavelet-Domain
Contextual Hidden Markov Tree Model”, International
Conference on Computer Science and Software
Engineering, IEEE, 2008.
[21] Wang Hong-Zhi Zhao Shuang Lv Hong-Wu, “SuperResolution Image Restoration with L-Curve”, IEEE
Congress on Image and Signal Processing, 2008.
[22] Wen-Hao Lee, Shang-Hong Lai, and Chia-Lun Chen,
“Iterative Blind Image Motion Deblurring Via
Learning A No-Reference Image Quality Measure”,
ICIP 2007.
[23] Xiaolei LU, Furong WANG, Hai HU, Benxiong
HUANG, “Wavelet domain image restoration and
parameters estimation based on variational Bayesian
method and Student-t priors”, IEEE International
Symposium on Computational Intelligence and
Design, 2008.
[24] Youshen Xia, and Mohamed S. Kamel, “Novel
Cooperative Neural Fusion Algorithms for Image
Restoration and Image Fusion”, IEEE Transactions On
Image Processing, Vol. 16, NO. 2, February 2007.
[25] Clerc M. and Kennedy J., “The Particle Swarm:
Explosion, Stability and Convergence in a MultiDimensional complex Space”, IEEE Transactions on
Evolutionary Computation, vol. 6, 2001.
[26] Dréo J. & Siarry P., Métaheuristiques pour
l’optimisation difficile, Edition Eyrolles, 2003.
[27] Kennedy J. and Eberhart R. “Particle Swarm
Optimization”, In Proceedings of IEEE International
Conference on Neural Networks, Perth, Australia, vol.
4, 1995.
[28] Kennedy J. & Eberhart R.C., Swarm Intelligence,
Morgan Kaufman Publishers, Academic Press, 2001.
[29] Omran M.G.H., Particle Swarm Optimization Methods
for Pattern Recognition and Image Processing, PhD
Thesis, University of Pretoria, November 2004.
[30] Van den Bergh F., An Analysis of Particle Swarm
Optimizers, PhD Thesis, Department of Computer
Science, University of Pretoria, South Africa, 2002.
[31] Vrahatis M.N., Parsopoulos K.E., “Parameter selection
and adaptation in Unified Particle Swarm
Optimization”, Mathematical and Computer
Modelling, vol. 46, 2007.
[32] Wu, Q.H., Ji, T.Y. and Lu, Z., “A Particle Swarm
Optimizer Applied to Soft Morphological Filters for
Periodic Noise Reduction”, Evo Workshops,
LNCS4448, 2007.
[33] A. Toumi, N. Rechid, A. Taleb-Ahmed, K.
Benmahammed, “Particle Swarm Optimization for
Image Deblurring”, 1st Mediterraniean Conference on
Intelligent Systems and Automation, Annaba, Algeria,
2008.
[34] A.M. Reynolds,” Cooperative random Lévy flight
searches and the flight patterns of honeybees", Physics
Letters A 354, 2006.
Comment citer
TOUMI, A . et al.
SEARCH EFFICIENCY FUNCTION-PSO COMBINATION FORBLIND IMAGE RESTORATION.
Courrier du Savoir, [S.l.], v. 13, mai 2014.
ISSN 1112-3338.
Disponible à l'adresse : >https://revues.univ-biskra.dz/index.php/cds/article/view/439>. Date de consultation : 15 nov. 2024
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