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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 previously used for image enhancement, but the linear filtering operation has generally been chosen heuristically. In this paper stochastic image models previously described and analyzed by the authors are used to model the true image and interfering components (shadows and salt-and-pepper noise). The problem of designing the linear filter can then be formulated as one of linear least mean-squared error (Wiener) filtering. Examples of processing of typical real-world images are included to indicate the obtainable results.