11 papers
Solving Inverse Problems with Flow-based Models via Model Predictive Control
George Webber, Alexander Denker, Riccardo Barbano +1
Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging. Recent work casts…
CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control
Riccardo Barbano, Alexander Denker, Zeljko Kereta +2
Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multiple pre-trained models remains a…
A Stability Benchmark of Generative Regularizers for Inverse Problems
Alexander Denker, Johannes Hertrich, Sebastian Neumayer
Generative (diffusion) priors demonstrate remarkable performance in addressing inverse problems in imaging. Yet, for scientific and medical imaging, it is crucial that reconstructi…
GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
James Rowbottom, Elizabeth L. Baker, Nick Huang +3
Score-based diffusion models in infinite-dimensional function spaces provide a mathematically principled framework for modelling function-valued data, offering key advantages such…
Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation
Serban Cristian Tudosie, Alexander Denker, Zeljko Kereta +1
Single-Pixel Imaging (SPI) enables the reconstruction of objects using a single detector through sequential illuminations with structured light patterns. The choice of illumination…
Deep Image Prior for Computed Tomography Reconstruction
Simon Arridge, Riccardo Barbano, Alexander Denker +1
We present a comprehensive overview of the Deep Image Prior (DIP) framework and its applications to image reconstruction in computed tomography. Unlike conventional deep learning m…