Since my bachelor thesis I decided to focus my attention on the wide image processing field. My first research experience was in the field of computational imaging where I was simulating unconventional camera systems. I was attracted by the unconventional thinking, hackiness and yet high level of mathematics applied to rather simple and familiar camera systems.
From the experiences of the thesis I continued the work as a student research assistant and initiated a project on computational glass free 3D displays. Tracing backwards some of the methods applied during the bachelor thesis and the project on computational displays lead me to the medical imaging domain and an internship at Siemens Healthineers, where I worked on deep learning based image quality assessment and artefact correction for magnetic resonance imaging.
Currently, I am exploring the deep learning landscape both on the application side as well as the theoretical side with some statistical learning theory. In parallel I am working on semantic segmentation of LIDAR scans for autonomous driving.
S. Braun, X. Chen, B. Odry, B. Mailhe, M. Nadar, “Motion Detection and Quality Assessment of MR images with Deep Convolutional DenseNets”, Proc. Intl. Soc. Mag. Reson. Med. 26:2715 (2018), Paris
S. Braun, P. Ceccaldi, X. Chen, B. Odry, B. Mailhe, M. Nadar, “Wasserstein GAN for Motion Artifact Reduction of MR images”, Proc. Intl. Soc. Mag. Reson. Med. 26:4093 (2018), Paris
Bachelor Thesis - Simulation einer Lichtfeldkamera mit Blendenmodulation