297 citations · 401 across the 24 of their papers we have counts for
13 papers · 1 filter
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Many modern iterative solvers for large-scale tomographic reconstruction incur two major computational costs per iteration: expensive forward/adjoint projections to update the data…
Data-driven methods for quantitative imaging
Guozhi Dong, Moritz Flaschel, Michael Hintermüller +3
In the field of quantitative imaging, the image information at a pixel or voxel in an underlying domain entails crucial information about the imaged matter. This is particularly im…
Machine Learning for Quantitative MR Image Reconstruction
Andreas Kofler, Felix Frederik Zimmermann, Kostas Papafitsoros
In the last years, the design of image reconstruction methods in the field of quantitative Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when dealing w…
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…
Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…
A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives
Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros
We propose and analyze a numerical algorithm for solving a class of optimal control problems for learning-informed semilinear partial differential equations. The latter is a class…
First-order conditions for the optimal control of learning-informed nonsmooth PDEs
Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros +1
In this paper we study the optimal control of a class of semilinear elliptic partial differential equations which have nonlinear constituents that are only accessible by data and a…
Optimization with learning-informed differential equation constraints and its applications
Guozhi Dong, Michael Hintermueller, Kostas Papafitsoros
Inspired by applications in optimal control of semilinear elliptic partial differential equations and physics-integrated imaging, differential equation constrained optimization pro…
Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization
Michael Hintermüller, Kostas Papafitsoros, Carlos N. Rautenberg +1
Total Generalized Variation (TGV) regularization in image reconstruction relies on an infimal convolution type combination of generalized first- and second-order derivatives. This…
Quantitative Magnetic Resonance Imaging: From Fingerprinting to Integrated Physics-Based Models
Guozhi Dong, Michael Hintermüller, Kostas Papafitsoros
Quantitative magnetic resonance imaging (qMRI) is concerned with estimating (in physical units) values of magnetic and tissue parameters e.g., relaxation times , , or pro…
Analysis and optimisation of a variational model for mixed Gaussian and Salt & Pepper noise removal
Luca Calatroni, Kostas Papafitsoros
We analyse a variational regularisation problem for mixed noise removal that was recently proposed in [14]. The data discrepancy term of the model combines and terms in…
A function space framework for structural total variation regularization with applications in inverse problems
Michael Hintermüller, Martin Holler, Kostas Papafitsoros
In this work, we introduce a function space setting for a wide class of structural/weighted total variation (TV) regularization methods motivated by their applications in inverse p…