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Found 5 papers, 2 papers with code
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution.
Learned reconstruction methods for inverse problems: sample error estimates
Learning-based and data-driven techniques have recently become a subject of primary interest in the field of reconstruction and regularization of inverse problems.
Learning the optimal Tikhonov regularizer for inverse problems
Then, we consider the problem of learning the regularizer from a finite training set in two different frameworks: one supervised, based on samples of both $x$ and $y$, and one unsupervised, based only on samples of $x$.
Convex regularization in statistical inverse learning problems
We consider a statistical inverse learning problem, where the task is to estimate a function $f$ based on noisy point evaluations of $Af$, where $A$ is a linear operator.
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
We propose a novel convolutional neural network (CNN), called $\Psi$DONet, designed for learning pseudodifferential operators ($\Psi$DOs) in the context of linear inverse problems.
