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Hardware architectures for real-time video enhancement and their application to an adaptive image sensor

The work presented in this thesis lies in the area of real-time video processing and focuses on the problem of enhancing in real-time the spatio-temporal resolution of the captured video sequence. To achieve the above, this thesis explores, proposes, and brings together, into a novel imaging system, appropriate imaging techniques related to different levels of processing: high-level video enhancement algorithms, low-level implementation on reconfigurable hardware, Field Programmable Gate Arrays (FPGA) in particular, and state of the art image sensor technology. Contrary to traditional cameras, which are passive, the proposed imaging system is dynamically configured in real-time according to the captured video data, based on the real-time interaction of an adaptive image sensor with an FPGA-based processing unit. The FPGA both configures the adaptive image sensor in a manner that maximizes the captured information, and further processes this raw data to render outputs of high resolution both in space and in time. Therefore, this work proposes possible sensor configurations and the appropriate processing methods for video enhancement under real-time constraints. These methods mainly include super-resolution and blur