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The problem of image enhancement by nonlinear two-dimensional (2-D) homomorphic filtering is approached using stochastic models of the signal and degradations. Homomorphic filtering has been used previously for image enhancement but the linear filtering operation has generally been chosen heuristically. In this paper stochastic image models described and analyzed previously by the authors are used to model the true image and interferring components (shadows and salt-and-pepper noise). The problem of designing the linear operation can then be formulated as one of linear least mean-squared error (Wiener) filtering. Examples of processing on typical real-world images are included to indicate the results obtainable.