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This paper introduces a Bayesian method for blind restoration of images of sparse, point-like objects. Examples of such images include astronomical star field frames and magnetoencephalogram imaging of current dipole distributions of brain neural activity. It is assumed that these images are corrupted by unknown blurring functions and noise. Both single and multiple frame observation cases are addressed. The proposed method uses maximum a posteriori estimation techniques to recover both the unknown object and blur. Markov random field (MRF) models are used to represent prior information about both the sparse, point-like structure of the object, and the smoothed random structure of the blur. As compared with general purpose blind algorithms, incorporating a sparse point source MRF model enables much higher resolution restorations, improves point localization, and aids in overcoming the convolutional ambiguity in the blind problem.