no code implementations • 28 Aug 2023 • Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
1 code implementation • 27 Aug 2023 • Imraj RD Singh, Alexander Denker, Riccardo Barbano, Željko Kereta, Bangti Jin, Kris Thielemans, Peter Maass, Simon Arridge
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
1 code implementation • 28 Mar 2023 • Marco Nittscher, Michael Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
1 code implementation • 20 Feb 2023 • Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Željko Kereta
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.
1 code implementation • 10 Oct 2022 • Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato
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.
1 code implementation • 11 Jul 2022 • Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction.
no code implementations • 17 Jun 2022 • Javier Antorán, David Janz, James Urquhart Allingham, Erik Daxberger, Riccardo Barbano, Eric Nalisnick, José Miguel Hernández-Lobato
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
2 code implementations • 28 Feb 2022 • Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment.
3 code implementations • 23 Nov 2021 • Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
no code implementations • pproximateinference AABI Symposium 2022 • Riccardo Barbano, Javier Antoran, José Miguel Hernández-Lobato, Bangti Jin
The deep image prior regularises under-specified image reconstruction problems by reparametrising the target image as the output of a CNN.
no code implementations • 22 Oct 2021 • Chen Zhang, Riccardo Barbano, Bangti Jin
Learned image reconstruction techniques using deep neural networks have recently gained popularity, and have delivered promising empirical results.
no code implementations • 6 Jul 2021 • Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann, Simon R. Arridge, Bangti Jin
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities.
no code implementations • 17 Nov 2020 • Riccardo Barbano, Željko Kereta, Chen Zhang, Andreas Hauptmann, Simon Arridge, Bangti Jin
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.
no code implementations • 20 Jul 2020 • Riccardo Barbano, Chen Zhang, Simon Arridge, Bangti Jin
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e. g., deep neural networks.