Unifying probabilistic and variational estimation

Abstract

this article, we present a variational approach to MAP estimation with a more qualitative and tutorial emphasis. The key idea behind this approach is to use geometric insight in helping construct regularizing functionals and avoiding a subjective choice of a prior in MAP estimation. Using tools from robust statistics and information theory, weshow that we can extend this strategy and develop two gradient descent flows for image denoising with a demonstrated performance

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