31 citations · 43 across the 7 of their papers we have counts for
9 papers
Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition
Franziska Schirrmacher, Benedikt Lorch, Anatol Maier +1
Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extrem…
Deep learning architectural designs for super-resolution of noisy images
Angel Villar-Corrales, Franziska Schirrmacher, Christian Riess
Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research. However, due to the amplification of noise during the upsampli…
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
Bernhard Stimpel, Christopher Syben, Franziska Schirrmacher +3
Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, thi…
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing
Prashant Chaudhari, Franziska Schirrmacher, Andreas Maier +2
High dynamic range (HDR) imaging combines multiple images with different exposure times into a single high-quality image. The image signal processing pipeline (ISP) is a core compo…
Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
Elisabeth Hoppe, Florian Thamm, Gregor Körzdörfer +6
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruct…
RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting
Elisabeth Hoppe, Florian Thamm, Gregor Körzdörfer +6
Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times $T…
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…
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…
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
Franziska Schirrmacher, Thomas Köhler, Lennart Husvogt +3
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spat…