OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery
OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery
复制标题
DOI：
--
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
T. Seets;Wei Lin;Yizhou Lu;Christie Lin;A. Uselmann;Andreas Velten
中科院分区：
文献类型：
--
作者：
T. Seets;Wei Lin;Yizhou Lu;Christie Lin;A. Uselmann;Andreas Velten
机器视觉和医学成像中的许多应用需要从具有非常低辐射的场景捕获图像，这可能导致非常嘈杂的图像和视频。这种应用的一个重要例子是荧光引导手术中荧光标记组织的成像。医学成像系统，特别是当预期用于手术时，被设计为在光线充足的环境中操作，并使用滤光器、时分或允许同时捕获场景的低辐射荧光视频和光线充足的可见光视频的其他策略。这项工作表明，通过利用深度学习以及来自无噪声视频的运动和纹理线索，可以显着改善视频去噪。
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.