Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery

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.