Big Data Challenges in X-Ray Microscopy at the Photon Limit

Supervisors: Prof. Christian Schroer (DESY, UHH), Prof. Mathias Trabs (formerly UHH)

The images acquired with any kind of optical instrument from visible light to hard x-ray regime are spoiled by noise and possibly some blurring. While in principle noise could be reduced by increasing the exposure time, i.e. the radiation dose is also increased, on the object being imaged, this might damage the object (especially biological specimen). Therefore, we have to recover the true image of the object from imperfect measurements. To this end so-called deconvolution and denoising techniques have been developed. In view of high resolution x-ray microscopy experiments, huge data sets have to be processed. Based on a rigorous mathematical understanding of the problem, the goal of this project is to improve the deconvolution methods and apply them to diffraction and full-field imaging data.