Search Results for author:
Found 9 papers, 1 papers with code
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing.
Single-Image based unsupervised joint segmentation and denoising
To this end, we combine the advantages of a variational segmentation method with the power of a self-supervised, single-image based deep learning approach.
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks.
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties.
Sparse aNETT for Solving Inverse Problems with Deep Learning
We propose a sparse reconstruction framework (aNETT) for solving inverse problems.
Deep synthesis regularization of inverse problems
For these deep learning methods, however, a solid theoretical foundation in the form of reconstruction guarantees is missing.
Augmented NETT Regularization of Inverse Problems
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems.
NETT: Solving Inverse Problems with Deep Neural Networks
Our theoretical results and framework are different from any previous work using neural networks for solving inverse problems.
Deep Learning for Photoacoustic Tomography from Sparse Data
In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data.
