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Improving Optical Flow on a Pyramid Level
Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi +3
In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow…
On the estimation of the Wasserstein distance in generative models
Thomas Pinetz, Daniel Soukup, Thomas Pock
Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training difficulties and th…
Image Morphing in Deep Feature Spaces: Theory and Applications
Alexander Effland, Erich Kobler, Thomas Pock +2
This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic…
Learned Collaborative Stereo Refinement
Patrick Knöbelreiter, Thomas Pock
In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the…
Self-Supervised Learning for Stereo Reconstruction on Aerial Images
Patrick Knöbelreiter, Christoph Vogel, Thomas Pock
Recent developments established deep learning as an inevitable tool to boost the performance of dense matching and stereo estimation. On the downside, learning these networks requi…
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
Alexander Effland, Erich Kobler, Karl Kunisch +1
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped b…
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
K S Sesh Kumar, Francis Bach, Thomas Pock
We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem a…
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock +1
Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple…
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Florian Knoll, Kerstin Hammernik, Chi Zhang +4
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic…