Short Communication: Localized Adversarial Artifacts for Compressed Sensing MRI

Solving Inverse Problems With Deep Neural Networks - Robustness Included

(2022) Martin Genzel et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

(2022) Yutong Chen et al.   PROCEEDINGS OF THE IEEE

The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem

(2022) Matthew J. Colbrook et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

On instabilities of deep learning in image reconstruction and the potential costs of AI

(2020) Vegard Antun et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Solving inverse problems using data-driven models

(2019) Simon Arridge et al.   ACTA NUMERICA

Image reconstruction by domain-transform manifold learning

(2018) Bo Zhu et al.   NATURE

Convolutional Neural Networks for Inverse Problems in Imaging: A Review

(2017) Michael T. McCann et al.   IEEE SIGNAL PROCESSING MAGAZINE

On the Role of Total Variation in Compressed Sensing

(2015) Clarice Poon   SIAM Journal on Imaging Sciences

Towards a Mathematical Theory of Super-resolution

(2013) Emmanuel J. Candès et al.   COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS

Exact reconstruction using Beurling minimal extrapolation

(2012) Yohann de Castro et al.   JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS

A Probabilistic and RIPless Theory of Compressed Sensing

(2011) Emmanuel J. Candes et al.   IEEE TRANSACTIONS ON INFORMATION THEORY

Compressed Sensing MRI

(2008) M. Lustig et al.   IEEE SIGNAL PROCESSING MAGAZINE