An Adaptive Model for Blind Image Restoration using Bayesian Approach

Authors: S.K. Satpathy, S.K. Nayak, K. K. Nagwanshi, S. Panda, C. Ardil

Abstract:

Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.

Keywords: Image Restoration, Probability DensityFunction (PDF), Neural Networks, Bayesian Classifier.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082433

References:

[1] H. C. Andrews & B. R. Hunt, "Digital Image restoration", Englewood cliffs, NJ, Prentice Hall, 1977.
[2] Y.T.Zhou, R. Chellappa and B.K. Jenkins, "Image restoration using a neural network," IEEE Trans. Acoust., Speech, Signal Processing, Vol, ASSP-36, pp 1141-1151, July 1988.
[3] J.L. Davidson and F. Hummer," Morphological neural networks: An introduction with applications," IEEE Systems Signal Processing, Vol 12, No.2, pp 177-210, 1993.
[4] Rafael C. Gonzalez and Richard E. Woods, "Digital image processing", 2nd edition, Addison- Wesely, 2004.
[5] Ling Zhang, Yun Zhang , Yi Min Yang : "Colour image restoration with multilayer Morphological Neural network", Proceedings of the second international conference on Machine learning and Cybernetics, Xian, November 2003
[6] M. R. Banham and A. K. Katsaggelos, "Digital Image Restoration,'' IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24-41, March 1997.
[7] D. Kundur and D. Hatzinakos, "Blind Image Deconvolution," IEEE Signal Processing Magazine, pp. 43-64, May 1997.
[8] N. P. Galatsanos and A. K. Katsaggelos, "Methods for Choosing the Regularization Parameter and Estimating the Noise Variance in Image Restoration and their Relation," IEEE Trans. on Image Processing, Vol. 1, No. 3, pp. 322-336, July 1992.
[9] Y. Yang, N. P. Galatsanos, and H. Stark, "Projection Based Blind Deconvolution," Journal of the Optical Society of America-A, Vol. 11, No. 9, pp. 2401-2409, September 1994.
[10] Joon K. Paik and Aggelos K. Katsaggelos, " Image restoration using a modified Hopfield Network", IEEE Transactions on image processing, Vol 1, No.1, pp. 49-63, January 1992.
[11] Y.T.Zhou, R. Chellappa and B.K. Jenkins, "Image restoration using a neural network," IEEE Trans. Acoust., Speech, Signal Processing, Vol, ASSP-36, pp 1141-1151, July 1988.
[12] H. C. Andrews & B. R. Hunt, "Digital Image restoration", Englewood cliffs, NJ, Prentice Hall, 1977.
[13] A. Likas, and N. M Galatsanos, "Variational Methods for Bayesian Blind Deconvolution", Proceedings of the IEEE International Conference on Image Processing (ICIP-03), September Barcelona, Spain 2003.
[14] Rafael C. Gonzalez and Richard E. Woods, "Digital image processing", 2nd edition, Addison- Wesely, 2004.
[15] Y.Won and P.D. Gader, " Morphological shared weight neural network for pattern classification and automatic target detection," in Proc. 1995 IEEE Int. Conf. Neural Networks, Perth, Australia, Vol 4, pp 2134-2138, Nov. 1995.