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<p>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.</p>
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

H. Mallikarjuna and L. Chaparro, "Iterative Composite Filtering for Image Restoration," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 674-678, 1992.
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