Search Results for author:
Found 8 papers, 6 papers with code
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data.
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?
Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable").
Solving Inverse Problems With Deep Neural Networks -- Robustness Included?
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems.
Interval Neural Networks as Instability Detectors for Image Reconstructions
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.
Interval Neural Networks: Uncertainty Scores
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.
