Super-resolution techniques for minimally invasive surgery

We propose the use of super-resolution techniques to aid visualization while carrying out minimally invasive surgical procedures. These procedures are performed using small endoscopic cameras, which inherently have limited imaging resolution. The use of higher-end cameras is technologically challenging and currently not yet cost effective. A promising alternative is to consider improving the resolution by postprocessing the acquired images through the use of currently prevalent super-resolution techniques. In this paper we analyse the different methodologies that have been proposed for super-resolution and provide a comprehensive evaluation of the most significant algorithms. The methods are evaluated using challenging in-vivo real world medical datasets. We suggest that the use of a learning-based super-resolution algorithm combined with an edge-directed approach would be most suited for this application.

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