
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problem...
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Simple singlescale microstructures based on optimal rank3 laminates
With the goal of identifying optimal elastic singlescale microstructure...
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Solving Equations of Random Convex Functions via Anchored Regression
We consider the question of estimating a solution to a system of equatio...
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Learning convex regularizers satisfying the variational source condition for inverse problems
Variational regularization has remained one of the most successful appro...
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SublabelAccurate Discretization of Nonconvex FreeDiscontinuity Problems
In this work we show how sublabelaccurate multilabeling approaches can ...
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Continuous Learning: Engineering Super Features With Feature Algebras
In this paper we consider a problem of searching a space of predictive m...
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Convex Regularization and Representer Theorems
We establish a result which states that regularizing an inverse problem ...
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Inverse Scale Space Iterations for NonConvex Variational Problems Using Functional Lifting
Nonlinear filtering approaches allow to obtain decompositions of images with respect to a nonclassical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely onehomogeneous regularizer. In order to extend these approaches to general energies with nonconvex data term, we apply the Bregman iteration to a lifted version of the functional with sublabelaccurate discretization. We provide a condition for the subgradients of the regularizer under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and nonconvex case.
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