no code implementations • 5 Mar 2024 • Buda Bajić, Johannes A. J. Huber, Benedikt Neyses, Linus Olofsson, Ozan Öktem
In the wood industry, logs are commonly quality screened by discrete X-ray scans on a moving conveyor belt from a few source positions.
1 code implementation • 15 Aug 2023 • Willem Diepeveen, Carlos Esteve-Yagüe, Jan Lellmann, Ozan Öktem, Carola-Bibiane Schönlieb
First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape.
no code implementations • 25 May 2023 • Paul Häusner, Ozan Öktem, Jens Sjölund
Finding suitable preconditioners to accelerate iterative solution methods, such as the conjugate gradient method, is an active area of research.
no code implementations • 20 Sep 2022 • Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Ramona Woitek, Cathal McCague, James D. Brenton, Ozan Öktem, Evis Sala, Leonardo Rundo
On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability.
1 code implementation • 12 Sep 2022 • Carlos Esteve-Yagüe, Willem Diepeveen, Ozan Öktem, Carola-Bibiane Schönlieb
The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each image corresponds to a different conformation of the macromolecule.
no code implementations • 5 Aug 2022 • Alma Eguizabal, Ozan Öktem, Mats U. Persson
In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network.
no code implementations • 11 Jun 2022 • Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Carola-Bibiane Schönlieb
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
no code implementations • 24 May 2022 • Jevgenija Rudzusika, Buda Bajić, Thomas Koehler, Ozan Öktem
To the best of our knowledge, this work is the first to apply an unrolled deep learning architecture for reconstruction on full-sized clinical data, like those in the Low dose CT image and projection data set (LDCT).
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Jevgenija Rudzusika, Buda Bajic, Ozan Öktem, Carola-Bibiane Schönlieb, Christian Etmann
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
no code implementations • 26 Aug 2021 • Jevgenija Rudzusika, Thomas Koehler, Ozan Öktem
This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning.
no code implementations • 12 Aug 2021 • Héctor Andrade-Loarca, Gitta Kutyniok, Ozan Öktem, Philipp Petersen
We present a deep learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging.
1 code implementation • NeurIPS 2021 • Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
1 code implementation • 30 Mar 2021 • Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb
In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning.
1 code implementation • 6 Aug 2020 • Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
1 code implementation • 27 Nov 2019 • Héctor Andrade-Loarca, Gitta Kutyniok, Ozan Öktem
This is based on the fact that edges in images contain most of the semantic information.
no code implementations • 26 Aug 2019 • Ozan Öktem, Camille Pouchol, Olivier Verdier
We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm.
1 code implementation • 1 Aug 2019 • Andreas Hauptmann, Jonas Adler, Simon Arridge, Ozan Öktem
Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
1 code implementation • 5 Jan 2019 • Héctor Andrade-Loarca, Gitta Kutyniok, Ozan Öktem, Philipp Petersen
Microlocal analysis provides deep insight into singularity structures and is often crucial for solving inverse problems, predominately, in imaging sciences.
no code implementations • 9 Dec 2018 • Chong Chen, Barbara Gris, Ozan Öktem
This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration.
1 code implementation • 14 Nov 2018 • Jonas Adler, Ozan Öktem
Characterizing statistical properties of solutions of inverse problems is essential for decision making.
no code implementations • 27 Aug 2018 • Jonas Adler, Sebastian Lunz, Olivier Verdier, Carola-Bibiane Schönlieb, Ozan Öktem
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem.
1 code implementation • 2 Aug 2018 • Sebastian Banert, Axel Ringh, Jonas Adler, Johan Karlsson, Ozan Öktem
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function.
Optimization and Control 90C25 (Primary) 68T05, 47H05 (Secondary)
2 code implementations • NeurIPS 2018 • Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.
1 code implementation • 30 Oct 2017 • Jonas Adler, Axel Ringh, Ozan Öktem, Johan Karlsson
We propose using the Wasserstein loss for training in inverse problems.
4 code implementations • 20 Jul 2017 • Jonas Adler, Ozan Öktem
We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
3 code implementations • 13 Jun 2017 • Chong Chen, Ozan Öktem
The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations.
5 code implementations • 13 Apr 2017 • Jonas Adler, Ozan Öktem
We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators.