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In this paper, we discuss a statistical framework for multiscale signal and image processing based on a class of multiresolution stochastic models, which can be used to represent spatial random processes at a range of scales. The model class is quite rich, and in fact includes the class of Markov random fields. In addition, the models have a scale recursive structure which naturally leads to efficient, scale recursive algorithms for smoothing and likelihood calculation. We discuss an application of the framework to the problem of computing optical flow in image sequence, and demonstrate computational savings on the order of one to two orders of magnitude over standard algorithms.
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