1 code implementation • 28 Oct 2023 • Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann, Bangti Jin, Peter Maass
A suite of performance metrics is employed to assess the efficacy of these methods.
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.
no code implementations • 23 Aug 2023 • Yongcheng Dai, Bangti Jin, Ramesh Sau, Zhi Zhou
In this work, we investigate a neural network based solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems.
no code implementations • 18 Aug 2023 • Bangti Jin, Zehui Zhou, Jun Zou
Furthermore, we develop an approach for approximating bi-Lipschitz maps on infinite-dimensional spaces that simultaneously approximate the forward and inverse maps, by combining model reduction with principal component analysis and INNs for approximating the reduced map, and we analyze the overall approximation error of the approach.
1 code implementation • 18 Apr 2023 • Siyu Cen, Bangti Jin, Kwancheol Shin, Zhi Zhou
Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject.
1 code implementation • 29 Mar 2023 • Bangti Jin, Xiyao Li, Qimeng Quan, Zhi Zhou
In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain.
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 • 7 Sep 2022 • Tianhao Hu, Bangti Jin, Zhi Zhou
Extensive numerical experiments in two- and multi-dimensional spaces with point sources, line sources or their combinations are presented to illustrate the efficiency of the proposed approach, and a comparative study with several existing approaches based on neural networks is also given, which shows clearly its competitiveness for the specific class of problems.
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 • 5 Apr 2022 • Bangti Jin, Xiyao Li, Xiliang Lu
Conductivity imaging represents one of the most important tasks in medical imaging.
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.
1 code implementation • 1 Aug 2019 • Chen Zhang, Bangti Jin
Aleatoric uncertainty is an intrinsic property of ill-posed inverse and imaging problems.
no code implementations • 18 Sep 2017 • Simon Arridge, Kazufumi Ito, Bangti Jin, Chen Zhang
In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior.
no code implementations • 3 Mar 2014 • Yuling Jiao, Bangti Jin, Xiliang Lu
We develop a primal dual active set with continuation algorithm for solving the \ell^0-regularized least-squares problem that frequently arises in compressed sensing.
no code implementations • 4 Oct 2013 • Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, Can Yang
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.