A new mathematical framework for modeling speckled imagery is introduced based on embedding the spatial correlation properties of speckled imagery, obtained from statistical optics, into a Markov-random-field (MRP) framework. The model is then used to perform speckle-noise reduction through the utilization of a global energy-minimization algorithm, which consists of simulated annealing in conjunction with the metropolis sampler algorithm. Comparative experimental results using both simulated as well as real synthetic-aperture-radar (SAR) imagery show that the proposed speckle-reduction technique outperforms existing speckle-noise filtering methods. This success is attributable to the ability of the proposed model to capture the physical spatial statistics of speckle within the confines of a MRF framework.