• ABIDA TOUMI Department of electrical engineering University Mohamed Khider Biskra, Algeria
  • NADJIBA TERKI Department of electrical engineering University Mohamed Khider Biskra, Algeria
  • ABDELMALIK TALEB AHMED LAMIH UMR CNRS UVHC 8530 Le Mont Houy 59313 Valenciennes Cedex 9, France


This work deals with the restoration of lossy compressed image by the use of a metaheuristic which is the Particle Swarm Optimization Algorithm. This algorithm was designed and adopted by the introduction of the Search Efficiency Function for the blind restoration of blurred images and has given excellent results. So, in the present paper we try to apply it in the enhancement of lossy decompressed images, and this application constitutes the contribution of this work. Images used have been compressed by two different compression methods, fractal and JPEG, and with different compression rates. The experimental results obtained were excellent.


[1] Baskurt, R. Prost, R. Goutte, “Iterative Constrained Restoration of DCT-Compressed Images”, Signal Processing 17, Elsevier Science Publishers B.V, 1989, pp. 201-211.
[2] C.C. Chang, F.C. Shien, Detection and restoration of tampered JPEG compressed images. The Journal of Systems and Software 64, Elsevier, 2002, pp151–161.
[3] J. Dréo and P. Siarry, Métaheuristiques pour l’optimisation difficile, Edition Eyrolles, 2003.
[4] Feng-qing Qin, Jun Min, Hong-rong Guo, “A Blind Image Restoration Method Based on PSF Estimation», World Congress on Software Engineering, 2009 IEEE.
[5] R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd ed., Prentice Hall, 2002.
[6] J. Kennedy, R.C. Eberhart, Swarm Intelligence, Morgan Kaufman Publishers, Academic Press, 2001.
[7] Lin Ma, Debin Zhao, Wen Gao, “Learning-based image restoration for compressed images”, Signal Processing:Image Communication, Elsevier, 2012.
[8] Lin Ma, Feng Wu, Debin Zhao, Wen Gao, and Siwei Ma, “Learning-Based Image Restoration for Compressed image through Neighboring Embedding”, PCM 2008, LNCS 5353, Springer-Verlag Berlin Heidelberg 2008.
[9] S. Mallat, A Wavelet Tour Of Signal Processing, 2nd ed., Academic Press, Elsevier (USA), 1999.
[10] A.M. Reynolds, «Cooperative random Lévy flight searches and the flight patterns of honeybees», Physics Letters A 354, 2006.
[11] S. Roth and M. Black, “Field of Experts: A Framework for Learning Image Priors”, in Proc. Computer Vision Pattern Recognition, vol. 2, pp. 680-867, 2005.
[12] D. Sun and W. K. Cham, “Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior”, IEEE Trans. Image Processing, vol. 16, 2007.
[13] A. Toumi, N. Rechid, A. Taleb-Ahmed, K. Benmahammed, “Two Ways of Use of the PSO for Degraded Image Restoration”, Journal of Communication and Computer, vol. 8, Number 6, pp. 436-442 June 2011.
[14] A. Toumi, N. Rechid, A. Taleb-Ahmed, K. Benmahammed, “Search Efficiency Function- PSO Combination for Blind Image Restoration”, SETIT 2011, Tunisia, 2011.
Comment citer
TOUMI, ABIDA; TERKI, NADJIBA; TALEB AHMED, ABDELMALIK. ADAPTIVE ALGORITHM FOR RESTORATION OF LOSSY COMPRESSED IMAGES. Courrier du Savoir, [S.l.], v. 21, nov. 2016. ISSN 1112-3338. Disponible à l'adresse : >>. Date de consultation : 02 jui. 2020