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Found 7 papers, 4 papers with code
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset.
Generative Sliced MMD Flows with Riesz Kernels
We prove that the MMD of Riesz kernels, which is also known as energy distance, coincides with the MMD of their sliced version.
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Iterative neural networks - which contain the physical model - can overcome these issues.
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems.
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation.
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation.
