Abstract
This paper proposes a restoration scheme for noisy images generated by coherent imaging systems (e.g., synthetic aperture radar, synthetic aperture sonar ultrasound imaging, and laser imaging). The approach is Bayesian: the observed image intensity is assumed to be a random variable with gamma density; the image to be restored (mean amplitude) is modeled by a compound Gauss-Markov random field which enforces smoothness on homogeneous regions while preserving discontinuities between neighboring regions. A Neyman-Pearson detection criterion is used to infer the discontinuities, thus allowing to select a given false alarm probability maximizing the detection probability. The whole restoration scheme is then cast into a maximum a posteriori probability (MAP) problem. An expectation maximization type iterative scheme embedded in a continuation algorithm is used to compute the MAP solution. An application example performed on radar data is presented.
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