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Found 16 papers, 9 papers with code
Analyzing Inexact Hypergradients for Bilevel Learning
Estimating hyperparameters has been a long-standing problem in machine learning.
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
One such approach uses generative models, trained on ground-truth images, as priors for inverse problems, penalizing reconstructions far from images the generator can produce.
Imaging with Equivariant Deep Learning
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry.
Regularising Inverse Problems with Generative Machine Learning Models
The success of generative regularisers depends on the quality of the generative model and so we propose a set of desired criteria to assess generative models and guide future research.
Equivariant neural networks for inverse problems
In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach.
Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
Here, we apply a recent dynamic accuracy derivative-free optimization method to hyperparameter tuning, which allows inexact evaluations of the learning problem while retaining convergence guarantees.
Multi-modality imaging with structure-promoting regularisers
Imaging with multiple modalities or multiple channels is becoming increasingly important for our modern society.
Inexact Derivative-Free Optimization for Bilevel Learning
A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user.
Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks.
Robust Image Reconstruction with Misaligned Structural Information
Multi-modality (or multi-channel) imaging is becoming increasingly important and more widely available, e. g. hyperspectral imaging in remote sensing, spectral CT in material sciences as well as multi-contrast MRI and PET-MR in medicine.
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".
Deep learning as optimal control problems: models and numerical methods
We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation.
Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins).
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality.
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable.
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming.
