activity
20172020
most citedBenchmarking Super-Resolution Algorithms on Real Data

11 citations · 21 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2020

Joint Super-Resolution and Rectification for Solar Cell Inspection

Mathis Hoffmann, Thomas Köhler, Bernd Doll +6

Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolutio…

cs.CV20189 cited

Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging

Thomas Köhler

The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical ima…

cs.CV2018

Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data

Thomas Köhler, Michel Bätz, Farzad Naderi +3

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled f…

cs.CV2018

Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems

Franziska Schirrmacher, Thomas Köhler, Christian Riess

Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the…

cs.CV2018

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests

Aline Sindel, Katharina Breininger, Johannes Käßer +3

Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this…

cs.CV2018

Temporal and volumetric denoising via quantile sparse image prior

Franziska Schirrmacher, Thomas Köhler, Tobias Lindenberger +7

This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various…