Adversarially learned iterative reconstruction for imaging inverse problems

DOI10.1007/978-3-030-75549-2_43zbMath1484.68192arXiv2103.16151OpenAlexW3164515561MaRDI QIDQ826227

Carola-Bibiane Schönlieb, Ozan Öktem, Subhadip Mukherjee

Publication date: 20 December 2021

Full work available at URL: https://arxiv.org/abs/2103.16151

zbMATH Keywords

inverse problemsunsupervised learningiterative reconstructiongenerative adversarial networks (GANs)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Numerical solution to inverse problems in abstract spaces (65J22)

Cites Work

• Variational methods in imaging
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Solving ill-posed inverse problems using iterative deep neural networks
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• NETT: solving inverse problems with deep neural networks
• Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems
• Solving inverse problems using data-driven models