Towards off-the-grid algorithms for total variation regularized inverse problems

DOI10.1007/978-3-030-75549-2_44zbMath1497.65044arXiv2104.06706MaRDI QIDQ826229

Romain Petit, Vincent Duval, Yohann de Castro

Publication date: 20 December 2021

Full work available at URL: https://arxiv.org/abs/2104.06706

zbMATH Keywords

total variationinverse problemsoff-the-grid imaging

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)

Related Items (1)

Off-the-Grid Curve Reconstruction through Divergence Regularization: An Extreme Point Result

Cites Work

• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Functions with generalized gradient and generalized surfaces
• Evolution of characteristic functions of convex sets in the plane by the minimizing total variation flow
• Sparsity of solutions for variational inverse problems with finite-dimensional data
• Sets of Finite Perimeter and Geometric Variational Problems
• Approximation of maximal Cheeger sets by projection
• Inverse problems in spaces of measures
• The sliding Frank–Wolfe algorithm and its application to super-resolution microscopy
• On Representer Theorems and Convex Regularization
• Towards a Mathematical Theory of Super‐resolution
• The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems
• Isoperimetric Inequalities in Mathematical Physics. (AM-27)
• Connected components of sets of finite perimeter and applications to image processing

This page was built for publication: Towards off-the-grid algorithms for total variation regularized inverse problems