297 citations · 401 across the 24 of their papers we have counts for
28 papers
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…
Learning spatially varying regularisation parameters of low regularity for image reconstruction
Kostas Papafitsoros, Luca Calatroni, Andreas Kofler
In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image reconstruction. Incorporating such…
Centering Ecological Goals in Automated Identification of Individual Animals
Lukas Picek, Timm Haucke, Lukáš Adam +16
Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent…
Degradation-based augmented training for robust individual animal re-identification
Thanos Polychronou, Lukáš Adam, Viktor Penchev +1
Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based on their fine-scale unique morpho…
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…
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Joshua Schulz, David Schote, Christoph Kolbitsch +2
State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness.…
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation
Thanh Trung Vu, Andreas Kofler, Kostas Papafitsoros
We extend a recently introduced deep unrolling framework for learning spatially varying regularisation parameters in inverse imaging problems to the case of Total Generalised Varia…
Learning Spatially Adaptive -Norms Weights for Convolutional Synthesis Regularization
Andreas Kofler, Luca Calatroni, Christoph Kolbitsch +1
We propose an unrolled algorithm approach for learning spatially adaptive parameter maps in the framework of convolutional synthesis-based regularization. More precisely,…
Why do we regularise in every iteration for imaging inverse problems?
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Regularisation is commonly used in iterative methods for solving imaging inverse problems. Many algorithms involve the evaluation of the proximal operator of the regularisation ter…
Nested Bregman Iterations for Decomposition Problems
Tobias Wolf, Derek Driggs, Kostas Papafitsoros +2
We consider the task of image reconstruction while simultaneously decomposing the reconstructed image into components with different features. A commonly used tool for this is a va…
WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals
Lukáš Adam, Vojtěch Čermák, Kostas Papafitsoros +1
This paper introduces WildlifeReID-10k, a new large-scale re-identification benchmark with more than 10k animal identities of around 33 species across more than 140k images, re-sam…
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…
WildlifeDatasets: An open-source toolkit for animal re-identification
Vojtěch Čermák, Lukas Picek, Lukáš Adam +1
In this paper, we present WildlifeDatasets (https://github.com/WildlifeDatasets/wildlife-datasets) - an open-source toolkit intended primarily for ecologists and computer-vision /…
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
Lukáš Adam, Vojtěch Čermák, Kostas Papafitsoros +1
This paper introduces the first public large-scale, long-span dataset with sea turtle photographs captured in the wild -- SeaTurtleID2022 (https://www.kaggle.com/datasets/wildlifed…
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…
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
Lukáš Adam, Vojtěch Čermák, Kostas Papafitsoros +1
This paper introduces the first public large-scale, long-span dataset with sea turtle photographs captured in the wild -- \href{https://www.kaggle.com/datasets/wildlifedatasets/sea…
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…
Bilevel training schemes in imaging for total-variation-type functionals with convex integrands
Valerio Pagliari, Kostas Papafitsoros, Bogdan Raiţă +1
In the context of image processing, given a -th order, homogeneous and linear differential operator with constant coefficients, we study a class of variational problems whose re…
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…
Variable step mollifiers and applications
Michael Hintermüller, Kostas Papafitsoros, Carlos N. Rautenberg
We consider a mollifying operator with variable step that, in contrast to the standard mollification, is able to preserve the boundary values of functions. We prove boundedness of…