Search Results for author: Carola-Bibiane Schönlieb

Found 89 papers, 32 papers with code

Unsupervised Learning of the Total Variation Flow

no code implementations9 Jun 2022 Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb

Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, a neural network approach to compute the solution of the TV flow given an initial image and a time instance.

Texture Classification

Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

no code implementations4 Apr 2022 Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb

We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.

Operator Sketching for Deep Unrolling Networks

no code implementations21 Mar 2022 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching.

Image Reconstruction

Multi-modal learning for predicting the genotype of glioma

no code implementations21 Mar 2022 Yiran Wei, Xi Chen, Lei Zhu, Lipei Zhang, Carola-Bibiane Schönlieb, Stephen J. Price, Chao Li

In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.

Unsupervised Clustering of Roman Potsherds via Variational Autoencoders

no code implementations14 Mar 2022 Simone Parisotto, Ninetta Leone, Carola-Bibiane Schönlieb, Alessandro Launaro

In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds.

Predicting conversion of mild cognitive impairment to Alzheimer's disease

no code implementations8 Mar 2022 Yiran Wei, Stephen J. Price, Carola-Bibiane Schönlieb, Chao Li

In this study, we develop a self-supervised contrastive learning approach to generate structural brain networks from routine anatomical MRI under the guidance of diffusion MRI.

Contrastive Learning

Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma

no code implementations14 Jan 2022 Yiran Wei, Chao Li, Xi Chen, Carola-Bibiane Schönlieb, Stephen J. Price

Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.

AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis

no code implementations23 Dec 2021 Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.

A Continuous-time Stochastic Gradient Descent Method for Continuous Data

no code implementations7 Dec 2021 Kexin Jin, Jonas Latz, ChenGuang Liu, Carola-Bibiane Schönlieb

Optimization problems with continuous data appear in, e. g., robust machine learning, functional data analysis, and variational inference.

Stochastic Optimization Variational Inference

Conditional Image Generation with Score-Based Diffusion Models

1 code implementation26 Nov 2021 Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann

Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.

Conditional Image Generation

Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.

Semantic Segmentation

Focal Attention Networks: optimising attention for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Leonardo Rundo, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.

Semantic Segmentation

Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation

1 code implementation31 Oct 2021 Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians.

Semantic Segmentation

Learning convex regularizers satisfying the variational source condition for inverse problems

no code implementations NeurIPS Workshop Deep_Invers 2021 Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger

Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades.

Stochastic Primal-Dual Deep Unrolling

no code implementations19 Oct 2021 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

We develop a stochastic (ordered-subsets) variant of the classical learned primal-dual (LPD), which is a state-of-the-art unrolling network for tomographic image reconstruction.

Computed Tomography (CT) Image Reconstruction

StyleGAN-induced data-driven regularization for inverse problems

no code implementations7 Oct 2021 Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers.

Image Inpainting Image Reconstruction +1

Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma

no code implementations21 Aug 2021 YiFan Li, Chao Li, Yiran Wei, Stephen Price, Carola-Bibiane Schönlieb, Xi Chen

In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.

Survival Prediction

LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification

1 code implementation8 Jun 2021 Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect.

Semi-Supervised Image Classification

HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

1 code implementation7 Jun 2021 Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.


Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

no code implementations16 May 2021 Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0. 878 and mean IoU of 0. 809, a 14% and 15% improvement over the previous state-of-the-art results of 0. 768 and 0. 702, respectively.

Semantic Segmentation

An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images

no code implementations10 May 2021 Julian D. Gilbey, Carola-Bibiane Schönlieb

Our experiments have shown that it is possible to perform OCR on 60 dpi scanned images of English text, which is a significantly lower resolution than the state-of-the-art, and we achieved a mean character level accuracy (CLA) of 99. 7% and word level accuracy (WLA) of 98. 9% across a set of about 1000 pages of 60 dpi text in a wide range of fonts.

Optical Character Recognition Super-Resolution

Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data

no code implementations27 Apr 2021 Madeleine Kotzagiannidis, Carola-Bibiane Schönlieb

In this work, we present a novel framework for the classification of HSI data in light of a very limited amount of labelled data, inspired by multi-view graph learning and graph signal processing.

graph construction Graph Learning

Adversarially learned iterative reconstruction for imaging inverse problems

1 code implementation30 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.

Image Reconstruction

Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

no code implementations2 Mar 2021 Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb

Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.

Equivariant neural networks for inverse problems

1 code implementation23 Feb 2021 Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry

In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach.

Inductive Bias

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

4 code implementations8 Feb 2021 Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions.

Medical Image Segmentation Semantic Segmentation

Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients

no code implementations5 Dec 2020 YiFan Li, Chao Li, Stephen Price, Carola-Bibiane Schönlieb, Xi Chen

Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes.

Survival Prediction

A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation

1 code implementation1 Dec 2020 Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy, Carola-Bibiane Schönlieb

The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion.

Semi-Supervised Semantic Segmentation

TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems

1 code implementation18 Nov 2020 Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb

In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.


Contrastive Registration for Unsupervised Medical Image Segmentation

1 code implementation17 Nov 2020 Lihao Liu, Angelica I Aviles-Rivero, Carola-Bibiane Schönlieb

Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.

Contrastive Learning Medical Image Segmentation +1

Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding

no code implementations8 Oct 2020 Veronica Corona, Yehuda Dar, Guy Williams, Carola-Bibiane Schönlieb

In this work we propose a framework for joint optimization of the MRI reconstruction and lossy compression, producing compressed representations of medical images that achieve improved trade-offs between quality and bit-rate.

Data Compression Image Compression +2

GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays

no code implementations30 Sep 2020 Angelica I. Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb, Nicolas Papadakis

The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.

A Linear Transportation $\mathrm{L}^p$ Distance for Pattern Recognition

no code implementations23 Sep 2020 Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis

The transportation $\mathrm{L}^p$ distance, denoted $\mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $\mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as multivariate time-series without normalisation or mass constraints.

Time Series

Unsupervised Image Restoration Using Partially Linear Denoisers

1 code implementation14 Aug 2020 Rihuan Ke, Carola-Bibiane Schönlieb

The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications.

Deblurring Image Denoising +1

Learned convex regularizers for inverse problems

no code implementations6 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.

Computed Tomography (CT) Deblurring

Ground Truth Free Denoising by Optimal Transport

1 code implementation3 Jul 2020 Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass

We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.


Deeply Learned Spectral Total Variation Decomposition

1 code implementation NeurIPS 2020 Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb

To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.

SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs

2 code implementations11 Jun 2020 Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield, Bambang Irawan, Eva Achmad, Muhammad Zudhi, Habibi, Elva Gemita, David A. Coomes

To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia.


Structure preserving deep learning

no code implementations5 Jun 2020 Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry

Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks.

Unsupervised clustering of Roman pottery profiles from their SSAE representation

no code implementations4 Jun 2020 Simone Parisotto, Alessandro Launaro, Ninetta Leone, Carola-Bibiane Schönlieb

The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches.

Multi-task deep learning for image segmentation using recursive approximation tasks

no code implementations26 May 2020 Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Mark Kirkland, Peter Schuetz, Carola-Bibiane Schönlieb

The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.

Multi-Task Learning Semantic Segmentation

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

no code implementations15 May 2020 Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek

This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.

Image Registration

On Learned Operator Correction in Inverse Problems

1 code implementation14 May 2020 Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen, Carola-Bibiane Schönlieb, Simon Arridge

We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.

iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling

2 code implementations11 May 2020 Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb

U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.

SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization

no code implementations27 Feb 2020 Derek Driggs, Junqi Tang, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb

We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.

Image Deconvolution Stochastic Optimization Optimization and Control 90C26

Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

1 code implementation ICML 2020 Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang

Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.


Total Variation Regularisation with Spatially Variable Lipschitz Constraints

1 code implementation5 Dec 2019 Martin Burger, Yury Korolev, Simone Parisotto, Carola-Bibiane Schönlieb

We introduce a first order Total Variation type regulariser that decomposes a function into a part with a given Lipschitz constant (which is also allowed to vary spatially) and a jump part.

Numerical Analysis Numerical Analysis 65J20, 65J22, 68U10, 94A08

Dynamic Spectral Residual Superpixels

no code implementations10 Oct 2019 Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb

We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.


A multi-task U-net for segmentation with lazy labels

no code implementations25 Sep 2019 Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.

Multi-Task Learning Semantic Segmentation

Learning the Sampling Pattern for MRI

2 code implementations20 Jun 2019 Ferdia Sherry, Martin Benning, Juan Carlos De los Reyes, Martin J. Graves, Georg Maierhofer, Guy Williams, Carola-Bibiane Schönlieb, Matthias J. Ehrhardt

The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".


On the Connection Between Adversarial Robustness and Saliency Map Interpretability

1 code implementation10 May 2019 Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb

Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.

Adversarial Robustness

Deep learning as optimal control problems: models and numerical methods

no code implementations11 Apr 2019 Martin Benning, Elena Celledoni, Matthias J. Ehrhardt, Brynjulf Owren, Carola-Bibiane Schönlieb

We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation.

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

1 code implementation14 Mar 2019 Philip Sellars, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb

A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.

Classification General Classification +2

A total variation based regularizer promoting piecewise-Lipschitz reconstructions

no code implementations12 Mar 2019 Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb, Christiane Stollenwerk

We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially).

Higher-Order Total Directional Variation. Part I: Imaging Applications

1 code implementation12 Dec 2018 Simone Parisotto, Jan Lellmann, Simon Masnou, Carola-Bibiane Schönlieb

We introduce a new class of higher-order total directional variation regularizers.

Numerical Analysis 47A52, 49M30, 49N45, 65J22, 94A08

Anisotropic osmosis filtering for shadow removal in images

1 code implementation17 Sep 2018 Simone Parisotto, Luca Calatroni, Marco Caliari, Carola-Bibiane Schönlieb, Joachim Weickert

We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al.~(Weickert, 2013) for visual computing applications, and we adapt it specifically to shadow removal applications.

Analysis of PDEs 68U10, 94A08, 49K20, 65M06,

Task adapted reconstruction for inverse problems

no code implementations27 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.

Image Reconstruction

Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning

1 code implementation21 Aug 2018 Matthias J. Ehrhardt, Pawel Markiewicz, Carola-Bibiane Schönlieb

Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins).

Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal

no code implementations29 May 2018 Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk

Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.

Reflection Removal

Adversarial Regularizers in Inverse Problems

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.


Unveiling the invisible - mathematical methods for restoring and interpreting illuminated manuscripts

1 code implementation19 Mar 2018 Luca Calatroni, Marie d'Autume, Rob Hocking, Stella Panayotova, Simone Parisotto, Paola Ricciardi, Carola-Bibiane Schönlieb

The last fifty years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering.

Image Restoration

Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation

2 code implementations4 Oct 2017 Leon Bungert, David A. Coomes, Matthias J. Ehrhardt, Jennifer Rasch, Rafael Reisenhofer, Carola-Bibiane Schönlieb

In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality.

Blind Super-Resolution Super-Resolution

Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications

2 code implementations15 Jun 2017 Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb

We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable.

Nonlinear Spectral Image Fusion

no code implementations23 Mar 2017 Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb

In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks.

Image Manipulation

A Variational Model for Joint Motion Estimation and Image Reconstruction

no code implementations12 Jul 2016 Martin Burger, Hendrik Dirks, Carola-Bibiane Schönlieb

The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model.

Image Reconstruction Motion Estimation

Bilevel approaches for learning of variational imaging models

1 code implementation8 May 2015 Luca Calatroni, Cao Chung, Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen

We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.

The structure of optimal parameters for image restoration problems

no code implementations8 May 2015 Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen

The analysis is done on the original -- in image restoration typically non-smooth variational problem -- as well as on a smoothed approximation set in Hilbert space which is the one considered in numerical computations.

Image Restoration

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