IMAGE RESTORATION - A New Explicit Approach in Filtering and Restoration of Digital Images

Pejman Rahmani, Benôıt Vozel, Kacem Chehdi

Abstract

Image restoration, in presence of noise, is well known to be an ill-posed inverse problem. Deconvolution of blurry and noisy digital images is a very active research area in image processing. This paper introduces a novel approach composed of two optimized sequential stages of image processing: denoising followed by deconvolution. In the first stage, the denoising filter and the number of iteration are chosen in order to obtain the best value of the usual criteria and the good recovering of the blurry image. We assume that the statistics of the noise are previously estimated. In the second stage, a deconvolution method is applied on an almost noise free version of the blurry image. Compared with the classical deconvolution methods, the numerical experiments of proposed method, appear to give significant improvement. The preliminary results of the new cascade approach are very encouraging as well.

References

  1. Biemond, J., Lagendijk, R. L., and Mersereau, R. M. (1990). Iterative methods for image deblurring. Proceedings of the IEEE, 78(5):856-883.
  2. Bronstein, M. M., Bronstein, A. M., Zibulevsky, M., and Zeevi, Y. Y. (2005). Blind deconvolution of images using optimal sparse representations. IEEE Transactions on Image Processing, 14(6):726-736.
  3. Chan, T., Osher, S., and Shen, J. (2001). The digital TV filter and nonlinear denoising. IEEE Transactions on Image Processing, 10(2):231-241.
  4. Chantas, G. K., Galatsanos, N. P., and Likas, A. (2006). Bayesian restoration using a new nonstationary edgepreserving image prior. IEEE Transactions on Image Processing, 15(10):2987-2997.
  5. Jalobeanu, A., Blanc-Feraud, L., and Zerubia, J. (2002). Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method. Pattern Recognition, 35(2):341-352.
  6. Klaine, L. (2004). Filtrage et restauration myopes des images numériques. PhD thesis, Université de Rennes 1, France.
  7. Koenderink, J. (1984). The structure of images. Biological Cybernetics, 50(5):363-370.
  8. Lee, J. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2(2):165-168.
  9. Likas, A. C. and Galatsanos, N. P. (2004). A Variational Approach for Bayesian Blind Image Deconvolution. IEEE Transactions on Image Processing, 52(8):2222- 2233.
  10. Mohammad-Djafari, A. (1996). Joint estimation of parameters and hyperparameters in a Bayesian approach of solving inverse problems. International Conference on Image Processing, Lausanne, Switzerland, 1:473- 476.
  11. Molina, R., Mateos, J., and Katsaggelos, A. K. (2006). Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation. IEEE Transactions on Image Processing, 15(12):3715-3727.
  12. Molina, R., Mateos, J., Katsaggelos, A. K., and Vega, M. (2003). Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Transactions on Image Processing, 12(12):1642- 1654.
  13. Nikolova, M., Idier, J., and Mohammad-Djafari, A. (1998). Inversion of large-support ill-posed linear operators using apiecewise Gaussian MRF. IEEE Transactions on Image Processing, 7(4):571-585.
  14. Park, S. C. and Kang, M. G. (2006). Noise-adaptive edge-preserving image restoration algorithm. Optical Engineering (Bellingham, Washington), 39(12):3124- 3137.
  15. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639.
  16. Rudin, L., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60(1-4):259-268.
Download


Paper Citation


in Harvard Style

Rahmani P., Vozel B. and Chehdi K. (2007). IMAGE RESTORATION - A New Explicit Approach in Filtering and Restoration of Digital Images . In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007) ISBN 978-989-8111-13-5, pages 196-199. DOI: 10.5220/0002140501960199


in Bibtex Style

@conference{sigmap07,
author={Pejman Rahmani and Benôıt Vozel and Kacem Chehdi},
title={IMAGE RESTORATION - A New Explicit Approach in Filtering and Restoration of Digital Images},
booktitle={Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)},
year={2007},
pages={196-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002140501960199},
isbn={978-989-8111-13-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)
TI - IMAGE RESTORATION - A New Explicit Approach in Filtering and Restoration of Digital Images
SN - 978-989-8111-13-5
AU - Rahmani P.
AU - Vozel B.
AU - Chehdi K.
PY - 2007
SP - 196
EP - 199
DO - 10.5220/0002140501960199