Enhancing the quality of tomographic reconstruction by advanced iterative algorithms optimized for parallel architectures

X-ray microtomography is a powerful tool to analyze and understand internal otherwise invisible mechanisms of small animals. Resolution and duration of experiments with living objects are currently limited by radiation damage. The compressed sensing theory has demonstrated the feasibility to recover signals from the under sampled data and, hence, opens up the possibility to reduce radiation. These reconstruction techniques are computationally very demanding and have therefore been not used for synchrotron experiments up to now.
The master thesis will be performed within an international project that aims to develop a novel instrumentation for ultrafast imaging at synchrotron light sources. We expect the student to get familiar with the latest developments in the field of compressive sensing theory and its application to tomographic image reconstruction. Advanced methods described in literature have to be evaluated using realistic sample datasets. Promising algorithms should to be implemented. To take advantage of the latest high-performance computing hardware, the selected algorithms have to be adapted for better mapping to massively parallel architectures. The implementation in OpenCL will be optimized for latest GPU architectures from AMD and NVIDIA.

Weitere Informationen

Unternehmen

Thesius Inspiration

Bereich/Abteilung

Institute for Data Processing and Electronics (IPE)

Abschlussart

Bachelorarbeit

Branche

Informationstechnologie und -dienste

Schlagwörter

X-ray microtomography GPU programming parallel programming non-linear optimization iterative image reconstruction tomographic image reconstruction compressive sensing theory Informatik

Anforderungen

Very good knowledge of the programming languages C and Python. Good knowledge of linear algebra and computer vision algorithms. Prior experience in parallel programming with CUDA, OpenCL, MPI, SIMD, OpenMP or Pthreads is a plus.

Zusatzinformationen

Bewerbung bis: 28.02.2015

Experience Gained
Synchrotron imaging, compressive sensing theory, iterative image reconstruction, non-linear optimization, parallel programming, GPU programming

Ansprechpartner/in für fachliche Fragen:

Fachliche Auskünfte erteilt Ihnen gerne Suren Chilingaryan, IPE, Phone: +49 721 608 26579 (suren.chilingaryan@kit.edu) or Andreas Kopmann, IPE, Phone: +49 721 608 24910 (andreas.kopmann@kit.edu)

Bewerbung:

Bitte senden Sie Ihre Bewerbung online unter Angabe der Stellenausschreibungsnummer IPE 13-14 an Frau Berger, Berufliche Ausbildung, Telefon 0721 608-25184.

Bei entsprechender Eignung werden schwerbehinderte Bewerber/innen bevorzugt berücksichtigt.

Karlsruher Institut für Technologie 01.09.2014