OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery

Trevor Seets, Wei Lin, Yizhou Lu, Christie Lin, Adam Uselmann, Andreas Velten

Keywords: Fluorescence guided surgery, video denoising, neural network denoising, deep learning, fluorescence video dataset, guided denoising

Abstract: Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video.