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Found 4 papers, 2 papers with code
Convergence Properties of Score-Based Models using Graduated Optimisation for Linear Inverse Problems
In this work, we show that score-based generative models (SGMs) can be used in a graduated optimisation framework to solve inverse problems.
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.
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.
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.
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