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 cam- eras is technologically challenging and currently not yet cost effective. A promising alternative is to consider improving the resolution by post- processing the acquired images through the use of currently prevalent super-resolution techniques. In this paper we analyse the different method- ologies 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 com- bined with an edge-directed approach would be most suited for this application.
Recommended citation: V. De Smet, V.P. Namboodiri and L. Van Gool (2011). “Super-resolution techniques for minimally invasive surgery”, 6th MICCAI workshop on augmented environments for computer assisted interventions-AE-CAI 2011 , Toronto,Canada.