Deep Image Inpainting to Support Endoscopic Procedures

Abstract

Deep image inpainting is a computer vision task that uses Deep Neural Networks to generate plausible content to complete an image, for example for the restoration of a damaged image or the removal of unwanted elements captured in the picture. This paper uses deep image inpainting to restore endoscopic images that are affected by various types of artifacts. To this end, we developed a transfer learning-based procedure that uses the CSA inpainting model, which was originally proposed for unrelated tasks including the restoration of images from the Paris StreetView Dataset. The proposed system is trained and validated on the EndoCV2020 dataset, consisting of images from real endoscopies, highlighting how deep image inpainting may be a promising technology for frame restoration during medical procedures.