Catalina Cocianu, Luminita State, Panayiotis Vlamos, Doru Constantin



The restoration can be viewed as a process that attempts to reconstruct or recover an image that has been degraded by using some a priori knowledge about the degradation phenomenon. The multiresolution support provides a suitable framework for noise filtering and image restoration by noise suppression. We present the algorithms GMNR, a generalization of the MNR algorithm based on the multiresolution support set for noise removal in case of arbitrary mean, and NFPCA. A comparative analysis of the performance of the algorithms GNMR and NFPCA is experimentally performed against the standard AMVR and MMSE.


  1. Bacchelli, S., Papi S. 2006, Image denoising using principal component analysis in the wavelet domain. In Journal of Computational and Applied Mathematics, Volume 189, Issues 1-2, 1 May 2006, Pages 606-621
  2. Balster, E. J., Zheng, Y. F., Ewing, R. L. 2003, Fast, Feature-Based Wavelet Shrinkage Algorithm for Image Denoising. In International Conference on Integration of Knowledge IntensiveMulti-Agent Systems. KIMAS 7803: Modeling, Exploration, and Engineering Held in Cambridge, MA on 30 September-October 4, 2003
  3. Cocianu, C., State, L., Stefanescu, V., Vlamos, P., 2006, PCA-Based Data Mining Probabilistic and Fuzzy Approaches with Applications in Pattern Recognition, Proceedings of ICSOFT 2006, Portugal, pp. 55-60, 2006
  4. Cocianu, C., State, L., Vlamos, P.,2002, On a Certain Class of Algorithms for Noise Removal in Image Processing:A Comparative Study, In Third IEEE Conference on Information Technology ITCC-2002, Las Vegas, Nevada, USA, April 8-10, 2002
  5. Cocianu, C., State, L., Stefanescu, V., Vlamos, P., 2004, On the Efficiency of a Certain Class of Noise Removal Algorithms in Solving Image Processing Tasks, In: Proceedings of the ICINCO 2004, Setubal, Portugal
  6. Diamantaras, K.I., Kung, S.Y., Principal Component Neural Networks: theory and applications, John Wiley &Sons, 1996
  7. Gonzales, R., Woods, R., Digital Image Processing, Prentice Hall, 2002
  8. Haykin, S., Neural Networks A Comprehensive Foundation, Prentice Hall,Inc. 1999
  9. Hyvarinen, A., Karhunen, J., Oja,E., Independent Component Analysis, John Wiley &Sons, 2001
  10. Hyvarinen, A., Hoyer, P., Oja, P., 1999. Image Denoising by Sparse Code Shrinkage,,
  11. Pitas, I., 1993, Digital Image Processing Algorithms, Prentice Hall
  12. Portilla, J. 2005, Image Restoration using Gaussian Scale Mixtures in Overcomplete Oriented Pyramids. In SPIE's International Symposium on Optical Science and Technology, SPIE's 50th Annual Meeting, Proc. of the SPIE, vol. 5914, pp. 468-82, San Diego, CA, Aug 2005
  13. Sonka, M., Hlavac, V., 1997, Image Processing, Analyses and Machine Vision, Chapman & Hall Computing
  14. Stark, J.L., Murtagh, F., Bijaoui, A., 1995, Multiresolution Support Applied to Image Filtering and Restoration, Technical Report
  15. State, L, Cocianu, C, Vlamos, P.., 2001, Attempts in Using Statistical Tools for Image Restoration Purposes, In Proceedings of SCI2001, Orlando, USA, July 22-25, 2001
  16. Umbaugh, S., 1998, Computer Vision and Image Processing, Prentice Hall

Paper Citation

in Harvard Style

Cocianu C., State L., Vlamos P. and Constantin D. (2008). DECORRELATION TECHNIQUES IN IMAGE RESTORATION . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008) ISBN 978-989-8111-60-9, pages 193-196. DOI: 10.5220/0001933901930196

in Bibtex Style

author={Catalina Cocianu and Luminita State and Panayiotis Vlamos and Doru Constantin},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)},

in EndNote Style

JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)
SN - 978-989-8111-60-9
AU - Cocianu C.
AU - State L.
AU - Vlamos P.
AU - Constantin D.
PY - 2008
SP - 193
EP - 196
DO - 10.5220/0001933901930196