2 research outputs found
Deinterlacing und zeitliche Skalierung im Umfeld der UHD Konvertierung
Diese Arbeit untersucht die zeitliche Interpolation von Videosignalen. Durch die
Einführung des Ultra High Definition Television Standard wird eine höhere Bildwiederholrate benötigz um
Judder-Artefakte zu vermeiden. In diesem Zusammenhang kommt der zeitlichen Interpolation von nicht nativem UHD Material eine besondere Bedeutung zu. Neben der räumlichen Interpolation ist diese essenziell. In dieser Arbeit wird eine Übersicht über die Anforderungen an UHD Video gegeben. Bewegungskompensierende
Verfahren für das Deinterlacing und Upscaling in Verbindung mit einer Phasenkorrelation werden behandelt. Ein MATLAB-Programm wird entwickelt mit dem ein Upscaling und Deinterlacing von HD Material durchgeführt werden kann
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An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images
The rapid progress in Adaptive Optics (AO) imaging, in the last decades, has had a transformative impact on the entire approach underpinning the investigations of retinal tissues. Capable of imaging the retina in vivo at the cellular level, AO systems have revealed new insights into retinal structures, function, and the origins of various retinal pathologies. This has expanded the field of clinical research and opened a wide range of applications for AO imaging. The advances in image processing techniques contribute to a better observation of retinal microstructures and therefore more accurate detection of pathological conditions. The development of automated tools for processing images obtained with AO allows for objective examination of a larger number of images with time and cost savings and thus facilitates the use of AO imaging as a practical and efficient tool, by making it widely accessible to the clinical ophthalmic community.
In this work, an image processing framework is developed that allows for enhancement of AO high-resolution retinal images and accurate detection of photoreceptor cells. The proposed framework consists of several stages: image quality assessment, illumination compensation, noise suppression, image registration, image restoration, enhancement and detection of photoreceptor cells. The visibility of retinal features is improved by tackling specific components of the AO imaging system, affecting the quality of acquired retinal data. Therefore, we attempt to fully recover AO retinal images, free from any induced degradation effects. A comparative study of different methods and evaluation of their efficiency on retinal datasets is performed by assessing image quality. In order to verify the achieved results, the cone packing density distribution was calculated and correlated with statistical histological data. From the performed experiments, it can be concluded that the proposed image processing framework can effectively improve photoreceptor cell image quality and thus can serve as a platform for further investigation of retinal tissues. Quantitative analysis of the retinal images obtained with the proposed image processing framework can be used for comparison with data related to pathological retinas, as well as for understanding the effect of age and retinal pathology on cone packing density and other microstructures