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Abstract ▼ AI-Helper

One approach to improved image restoration methods has been the incorporation of additional source information via Gibbs priors that assume a source that is piecewise smooth. A natural Gibbs prior for expressing such constraints is an energy function defined on binary valued line processes as well a...

One approach to improved image restoration methods has been the incorporation of additional source information via Gibbs priors that assume a source that is piecewise smooth. A natural Gibbs prior for expressing such constraints is an energy function defined on binary valued line processes as well as source intensities. However, the estimation of both continuous variables and binary variables is known to be a difficult problem. In this work, we consider the application of the deterministic annealing method. Unlike other methods, the deterministic annealing method offers a principled and efficient means of handling the problems associated with mixed continuous and binary variable objectives. The application of the deterministic annealing method results in a sequence of objective functions (defined only on the continuous variables) whose sequence of solutions approaches that of the original mixed variable objective function. The sequence is indexed by a control parameter (the temperature). The energy functions at high temperatures are smooth approximations of the energy functions at lower temperatures. Consequently, it is easier to minimize the energy functions at high temperatures and then track the minimum through the variation of the temperature. This is the essence of a continuation method. We show experimental results, which demonstrate the efficacy of the continuation method applied to a Bayesian restoration model.

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