M22: Inverse Problems with Data-Driven Methods and Deep Learning
We are currently experiencing a paradigm shift in image reconstruction. Robust mathematical inversion algorithms are combined with emerging methods in data science to achieve state-of-the-art results in a wide range of inverse problems. A successful application of these methods in practice involves a thorough understanding of their mathematical properties and the underlying imaging physics. This minisymposium aims to bring experts in data-driven methods and deep learning for inverse problems together and provides an overview of learned image reconstruction approaches, mathematical insights, and real-world applications.
Organizers:
Tatiana Bubba, University of Bath, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Martin Genzel, Helmholtz-Zentrum Berlin für Materialien und Energie, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Andreas Hauptmann, University of Oulu, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Maximilian März, Technical University of Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Invited Speakers :
Vegard Antun, University of Oslo, Norway, This email address is being protected from spambots. You need JavaScript enabled to view it.
Still no free lunch – On AI generated hallucinations and the accuracy-stability trade-off in inverse problems
Riccardo Barbano, University College London, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
A Probabilistic Deep Image Prior for Computational Tomography
Sören Dittmer, University of Cambridge, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Ground truth free denoising by optimal transport
Margaret Duff, University of Bath, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Regularising Inverse Problems with Generative Machine Learning Models
Samy Wu Fung, Colorado School of Mines, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Efficient Training of Infinite-Depth Neural Networks via Jacobian-Free Backpropagation
Reinhard Heckel, Technical University of Munich, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Measuring and enhancing robustness in deep learning-based compressive sensing
Johannes Hertrich, TU Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Stochastic normalizing flows for inverse problems: a Markov chains viewpoint
Peter Jung, TU Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Solving MMV Problems via Algorithm Unfolding
Ulugbek Kamilov, Washington University, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
Maximilian Kiss, CWI, Netherlands, This email address is being protected from spambots. You need JavaScript enabled to view it.
2DeteCT: A large 2D expandable, trainable, experimental computed tomography data collection for machine learning
Jan Macdonald, TU Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Solving Inverse Problems With Deep Neural Networks - Robustness Included?
Subhadip Mukherjee, University of Cambridge, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Data-driven adversarial regularization for imaging inverse problems
Jenni Poimala, University of Oulu, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Learned speed of sound correction for photoacoustic tomography
Tom Tirer, Tel-Aviv University, Israel, This email address is being protected from spambots. You need JavaScript enabled to view it.
Solving Ill-Posed Inverse Problems with Pretrained Denoisers, GANs and Super-Resolvers: The BP Term and the Correction Filter
