Regularization with Sparse Vector Fields: From Image Compression to TV-type Reconstruction
From MaRDI portal
Publication:3300299
DOI10.1007/978-3-319-18461-6_16zbMath1444.94006arXiv1503.02044OpenAlexW1856345736MaRDI QIDQ3300299
Eva-Maria Brinkmann, Joana Sarah Grah, Martin Burger
Publication date: 28 July 2020
Published in: Lecture Notes in Computer Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1503.02044
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items
Inverse scale space decomposition ⋮ Summability estimates on transport densities with Dirichlet regions on the boundaryviasymmetrization techniques ⋮ Unified models for second-order TV-type regularisation in imaging: a new perspective based on vector operators
Cites Work
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Minimizing total variation flow
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Higher-order TV methods -- enhancement via Bregman iteration
- Nonlinear inverse scale space methods
- Ground states and singular vectors of convex variational regularization methods
- Shapes and Geometries
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
- High-Order Total Variation-Based Image Restoration
- Structural Properties of Solutions to Total Variation Regularization Problems
- Inverse problems in spaces of measures
- The Jump Set under Geometric Regularization. Part 1: Basic Technique and First-Order Denoising
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Total Generalized Variation