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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…
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…
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Alexander Denker, Moshe Eliasof, Zeljko Kereta +1
Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow ou…
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…
Plug-and-Play Half-Quadratic Splitting for Ptychography
Alexander Denker, Johannes Hertrich, Zeljko Kereta +3
Ptychography is a coherent diffraction imaging method that uses phase retrieval techniques to reconstruct complex-valued images. It achieves this by sequentially illuminating overl…