Local Kernels that Approximate Bayesian Regularization and Proximal Operators

9 Mar 2018 Frank Ong Peyman Milanfar Pascal Getreuer

In this work, we broadly connect kernel-based filtering (e.g. approaches such as the bilateral filters and nonlocal means, but also many more) with general variational formulations of Bayesian regularized least squares, and the related concept of proximal operators. The latter set of variational/Bayesian/proximal formulations often result in optimization problems that do not have closed-form solutions, and therefore typically require global iterative solutions... (read more)

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