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Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance.
Provably Convergent Data-Driven Convex-Nonconvex Regularization
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.
Joint reconstruction-segmentation on graphs
Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations.
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