Franziska Schirrmacher, M. Sc.
Below you will find my recent projects. Within the last 2 years I worked on super-resolution of indecipherable license plates and license plate recognition. In my Master Thesis I developed the Quantile Sparse Image (QuaSI) prior and its adaptive version (AQuaSI) under the supervision of Thomas Köhler.
License Plate Recognition:
Due to the recent success of neural networks for image classification tasks, we investigate the performance of neural networks on images of highly degraded license plates for character recognition.
Super-resolution of indecipherable license plates:
In police investigations, a severely degraded image quality leads to an impairment of their success. License plates might give a hint on the suspect, but are often indecipherable. The degradation of the image quality is mainly caused by compression artifacts, low contrast and a low resolution. In order to improve the image quality such that the license plate can be deciphered, super-resolution algorithms are applied to video frames in a post-processing step. Unfortunately, the registration of the frames is not sufficiently accurate to obtain visually pleasing images. Additionally, compression often leads to a loss of information such that super-resolution is not successful.
(Adaptive) Quantile Sparse Image Prior:
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 solution space. A key challenge in research on image processing is to find suited priors to represent natural images. We propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.
Open topics for Bachelor and Master theses:
In case you are interested in license plate recognition or to learn high dimensional filters from data please contact me via mail. For questions regarding other topics in the field of multimedia security you can also write a mail.
- Hoppe, E., Thamm, F., Körzdörfer, G., Syben, C., Schirrmacher, F., Nittka, M.,... Maier, A. (2019). Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks. In Rainer Röhrig, Harald Binder, Hans-Ulrich Prokosch, Ulrich Sax, Irene Schmidtmann, Susanne Stolpe, Antonia Zapf (Eds.), German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine. (pp. 126-133). IOS Press.
- Hoppe, E., Thamm, F., Syben, C., Schirrmacher, F., & Maier, A. (2019). RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. (pp. 92-100).
- Stimpel, B., Syben, C., Schirrmacher, F., Hoelter, P., Dörfler, A., & Maier, A. (2019). Multi-Modal Super-Resolution with Deep Guided Filtering. In Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 110-115). Lübeck, DE: Springer Berlin Heidelberg.
- Schirrmacher, F., Köhler, T., Endres, J., Lindenberger, T., Husvogt, L., Fujimoto, J.G.,... Maier, A. (2018). Temporal and Volumetric Denoising via Quantile Sparse Image Prior. Medical Image Analysis, 48(0), 131-146. https://dx.doi.org/10.1016/j.media.2018.06.002
- Schirrmacher, F., Köhler, T., Husvogt, L., Fujimoto, J.G., Hornegger, J., & Maier, A. (2018). Abstract: QuaSI - Quantile Sparse Image A Prior for Spatio-Temporal Denoising of Retinal OCT Data. Paper presentation at Bildverarbeitung für die Medizin 2018, Erlangen.
- Schirrmacher, F., Köhler, T., & Riess, C. (2018). Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems.
- Schirrmacher, F., Köhler, T., Husvogt, L., Fujimoto, J.G., Hornegger, J., & Maier, A. (2017). QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, Proceedings, Part II (pp. 83-91). Quebec City, QC, Canada: Springer Verlag.
- Schirrmacher, F., Taubmann, O., Unberath, M., & Maier, A. (2017). Towards Understanding Preservation of Periodic Object Motion in Computed Tomography. In Bildverarbeitung für die Medizin 2017 (pp. 116-121). DKFZ, Heidelberg: Springer Vieweg: Springer Vieweg.