Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. However, to accomplish any of these tasks, an estimate of the degradation model parameters is essential. We describe a parameter estimation algorithm for a morphological, binary image degradation model. Inputs to the estimation algorithm are the ideal and degraded images. We search for the optimal parameter by looking for a parameter value for which the corresponding noise pattern distribution in the simulated image and the given degraded image are most similar. The parameter space is searched using the downhill simplex algorithm of Nelder and Mead (1965). We use the p-value of the Kolmogorov-Smirnov test of difference between the two pattern distributions as the objective function value. We show results of applying our algorithm on document images.
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