论文标题
学会更快地看到：用深度不充分的图像降级来推动高速相机的极限
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
论文作者
论文摘要
以高获取率记录高保真视频的能力对于研究快速移动现象的研究至关重要。想象快速移动场景的困难在于运动模糊和不渗透噪音之间的权衡：一方面，记录长期暴露时间会遭受由记录场景中的运动引起的运动模糊效应。另一方面，随着曝光时间的范围，到达相机光传感器的光量减少，因此短期暴露记录遭受了不流行的噪音。在本文中，我们建议通过将高速成像的问题视为一种不受欢迎的图像降解问题来解决这一权衡。我们结合了使用深度学习对未充满刺激的图像降解的最新进展，并将这些方法适应了高速成像问题的特异性。我们的方法利用特定于传感器的噪声模型来利用大型外部数据集，可以在一个数量级上加速高速摄像头的采集速率，同时保持相似的图像质量。
The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena. The difficulty of imaging fast moving scenes lies in a trade-off between motion blur and underexposure noise: On the one hand, recordings with long exposure times suffer from motion blur effects caused by movements in the recorded scene. On the other hand, the amount of light reaching camera photosensors decreases with exposure times so that short-exposure recordings suffer from underexposure noise. In this paper, we propose to address this trade-off by treating the problem of high-speed imaging as an underexposed image denoising problem. We combine recent advances on underexposed image denoising using deep learning and adapt these methods to the specificity of the high-speed imaging problem. Leveraging large external datasets with a sensor-specific noise model, our method is able to speedup the acquisition rate of a High-Speed Camera over one order of magnitude while maintaining similar image quality.
