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The nonstationarity of images and the lack of a factorization theorem in two dimensions make the implementation of recursive imagerestoration procedures very difficult. A restoration procedure that partially avoids these issues by modeling the image differently is presented. The image is decomposed into additive components, and each one is modeled as a second-order random process with a separable exponential autocovariance and a constant mean. These components are obtained by means of a masking function relating the regional luminance in the image and the image sensitivity to noise. The present model facilitates the application of a two-dimensional Wiener filter in the form of a bank of filters. A restoration example illustrating the issues involved is given.