Maskierte Erfassung und inpaintingbasierte Wiederherstellung von großen Datenvolumen in Systemen mit eingeschränkter Berechnungskapazität

Abstract

The exponential growth of the generated data volume, due to the increasing number of participants in data traffic as well as networking, confronts many application-specific systems worldwide with new and future challenges. Based on the large amount, the high speed and the large variety of data, individual problems arise in the acquisition, transmission and processing. Managing such data volumes with traditional data processing approaches is often infeasible to resource-constrained applications, because of limitations of the computational capacity. This thesis focusses on solutions for the processing of high data volumes in systems with limited computational capacity. The key concept is based on a methodology for the asymmetric distribution of computational effort through the use of algorithmic tools in the field of multidimensional digital data restoration (inpainting). The combination of masking for data reduction and inpainting for data recovery opens up the potential to innovative solutions for currently unresolved problems and to improve existing approaches. From the field of medicine, space technology as well as imaging recording media, problems to evaluate the developed solutions of the innovative methodology in this thesis. Furthermore, the approaches are compared to different reference methods. Both, the degree of data reduction and the reconstruction quality of the inpainting-based recovery, serve as figures of merit to evaluate the developed approaches within simulated examinations of the application-specific scenarios.