Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
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DOI：
10.48550/arxiv.2211.16034
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Weihao Zhuang;T. Hascoet;R. Takashima;T. Takiguchi
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文献类型：
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作者：
Weihao Zhuang;T. Hascoet;R. Takashima;T. Takiguchi
在高采集率下录制高保真视频的能力是研究快速移动现象的核心。快速运动场景成像的困难在于运动模糊和曝光不足噪声之间的权衡：一方面，长曝光时间的记录会受到所记录场景中的运动引起的运动模糊效果的影响。另一方面，到达相机光电传感器的光量随着曝光时间的增加而减少，因此短曝光记录会受到曝光不足噪声的影响。在本文中，我们建议通过将高速成像问题视为曝光不足的图像去噪问题来解决这一权衡问题。我们结合了使用深度学习在曝光不足图像去噪方面的最新进展，并使这些方法适应高速成像问题的特殊性。利用具有传感器特定噪声模型的大型外部数据集，我们的方法能够将高速摄像机的采集速度加快一个数量级以上，同时保持类似的图像质量。
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.