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A method based on the particle filter for recovering images degraded by film-grain noise is proposed. Due to the nonlinear relationship between the silver density and exposure, film-grain noise manifests itself as multiplicative non-Gaussian noise in the exposure domain. Since the posterior density is non-Gaussian, the proposed method works by representing it by a set of samples with associated weights. These samples are propagated in a recursive framework to obtain an optimal estimate of the original image. The effectiveness of the method is demonstrated with examples.