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Iterative Composite Filtering for Image Restoration
June 1992 (vol. 14 no. 6)
pp. 674-678

An algorithm solution to the noisy image restoration problem under assumptions that the image is nonstationary and that the noise process is a superposition of white and impulsive noises is proposed. A composite model is used for the image in order to consider its nonstationarities, in the mean and the autocorrelation. Separating the gross information about the image from its textural information, the authors exploit the advantages of median, range, and Levinson filters in restoring the image. Median statistics are used to estimate the image's gross information and to filter the impulsive noise. Range statistics are used to segment the textural image into approximately locally stationary images to be filtered by Levenson filters. The proposed restoration algorithm adapts to the nonstationarity of the image, and, thus, it performs well. The algorithm is compared with others based on either median or linear filtering alone.

[1] T. Nodes, G. Liao, and N. Gallager, "Statistical analysis of two-dimensional median filtered images," inProc. IEEE ICASSP-1984, vol. 2.
[2] G. R. Arce and M. P. McLoughlin, "Theoretical analysis of the max/median filter,"IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-35, Jan. 1987.
[3] P. M. Narendra, "A separable median filter for image noise smoothing,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-3, pp. 20-29, Jan. 1981.
[4] S. Dravida, J. W. Woods, and W. C. Shen, "A comparison of image filtering algorithms," inProc. IEEE ICASSP-1984, vol. 2.
[5] R. N. Strickland, "Transforming images into block stationary behavior,"Appl. Optics, vol. 22, pp. 1462-1473, May 1983.
[6] J. S. Lee, "Digital image enhancement and noise filtering by use of local statistics."IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-2, pp. 165-168, 1980.
[7] D. T. Kuan, A. A. Sawchuck, T. C. Strand, and P. Chavel, "Adaptive noise smoothing filters for images with signal dependent noise,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-7, pp. 165-177, March 1985.
[8] G. E. Box and G. M. Jenkins,Time Series Analysis. San Francisco, CA: Holden-Day, 1976.
[9] M. Boudaoud, "Modeling of one and two dimensional nonstationary signals," Ph.D. dissertation, Univ. Pittsburgh, Pittsburgh, PA, 1986.
[10] M. Boudaoud and L. F. Chaparro, "Nonstationary composite modeling of images,"IEEE Trans. Syst. Man Cybern., vol. 19, pp. 112-117, Jan/Feb 1989.
[11] H. A. David,Order Statistics. New York: Wiley, 1981.
[12] A. M. Tekalp, H. Kaufman, and J. W. Woods, "Fast recursive estimation of the parameters of a space-varying autoregressive image model,"IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-35, pp. 469-472, Apr. 1985.
[13] B. D. O. Anderson and J. B. Moore,Optimal Filtering. Englewood Cliffs, NJ: Prentice Hall, 1979.

Index Terms:
picture processing; range statistics; white noise; iterative composite filtering; median filters; range filters; image restoration; impulsive noises; nonstationarities; autocorrelation; gross information; textural information; Levinson filters; correlation methods; filtering and prediction theory; noise; picture processing; statistics
Citation:
H.S. Mallikarjuna, L.F. Chaparro, "Iterative Composite Filtering for Image Restoration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 6, pp. 674-678, June 1992, doi:10.1109/34.141561
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