14 October 1997 Robustness of optimal binary filters for sparse noise
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Abstract
An optimal binary image filter is an operator defined on an observed random set (image) and the output random set estimates some ideal (uncorrupted) random set with minimal error. Assuming the probability law of the ideal process is determined by a parameter vector, the output law is also determined by a parameter vector, and this latter law is a function of the input law and a degradation operator producing the observed image from the ideal image. The robustness question regards the degree to which performance of an optimal filter degrades when it is applied to an image process whose law differs (not too greatly) form the law of the process for which it is optimal. The present paper examines robustness of the optimal translation-invariant binary filter for restoring images degraded by sparse salt-and-pepper noise. An analytical model is developed in terms of prior probabilities of the signal and this model is used to compute a robustness surface.
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Edward R. Dougherty, Edward R. Dougherty, Artyom M. Grigoryan, Artyom M. Grigoryan, } "Robustness of optimal binary filters for sparse noise", Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.290275; https://doi.org/10.1117/12.290275
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