“Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network” has been accepted

The paper “Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network” has been accepted for publication at the “IEEE International Symposium on Biomedical Imaging”, located at Iowa City, Iowa, USA (April 3-7, 2020). This conference is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS).
Authors: Negin Ghamsarian, Klaus Schoeffmann, Mario Taschwer

Abstract: A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement.

Keywords: Video Deblurring, Deconvolutional Neural Networks, Cataract Surgery Videos

Acknowledgment: This work was funded by the FWF Austrian Science Fund under grant P 31486-N31