Evaluation of super-resolution methods in the context of colonic polyp classification

In this work we investigate whether it is possible to improve the results of an automated classification of colonic polyps by using super-resolution algorithms on endoscopic video sequences. For this purpose we apply different super-resolution methods to endoscopic sequences and use a set of feature extraction methods for the classification of the SR reconstruction results. We then compare the results obtained from these experiments against the classification results based on original low-resolution frames and against classification rates based on upscaled versions of low-resolution frames. We show that, at least for the set of super-resolution methods and feature extraction methods evaluated, applying superresolution methods to the low-resolution frames has no significant impact on the resulting overall classification results.