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Found 14 papers, 7 papers with code
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
Image Reconstruction via Deep Image Prior Subspaces
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data.
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method.
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction.
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
A Probabilistic Deep Image Prior over Image Space
The deep image prior regularises under-specified image reconstruction problems by reparametrising the target image as the output of a CNN.
Conditional Variational Autoencoder for Learned Image Reconstruction
Learned image reconstruction techniques using deep neural networks have recently gained popularity, and have delivered promising empirical results.
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities.
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction.
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e. g., deep neural networks.
