Zusammenfassung
In the field of image-guided surgery, Time-of-Flight (ToF) sensors are of interest due to their fast acquisition of 3-D surfaces. However, the poor signal-to-noise ratio and low spatial resolution of today’s ToF sensors require preprocessing of the acquired range data. Superresolution is a technique for image restoration and resolution enhancement by utilizing information from successive raw frames of an image sequence. We propose a super-resolution framework using the graphics processing unit. Our framework enables interactive frame rates, computing an upsampled image from 10 noisy frames of 200 × 200 px with an upsampling factor of 2 in 109 ms. The root-mean-square error of the super-resolved surface with respect to ground truth data is improved by more than 20 % relative to a single raw frame.
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Wetzl, J., Taubmann, O., Haase, S., Köhler, T., Kraus, M., Hornegger, J. (2013). GPU-Accelerated Time-of-Flight Super-Resolution for Image-Guided Surgery. In: Meinzer, HP., Deserno, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2013. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36480-8_6
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DOI: https://doi.org/10.1007/978-3-642-36480-8_6
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