An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
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2026 3verdicts
UNVERDICTED 3representative citing papers
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
Numerical benchmarks indicate generative regularizers deliver strong reconstructions in some imaging inverse problem settings but can be unstable or problematic under imperfect conditions compared to variational methods.
citing papers explorer
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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography
An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
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Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
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A Stability Benchmark of Generative Regularizers for Inverse Problems
Numerical benchmarks indicate generative regularizers deliver strong reconstructions in some imaging inverse problem settings but can be unstable or problematic under imperfect conditions compared to variational methods.