Many important physical phenomena involve subtle signals that are difficult to observe with the unaided eye, yet visualizing them can be very informative. Current motion magnification techniques can reveal these small temporal variations in video, but require precise prior knowledge about the target signal, and cannot deal with interference motions at a similar frequency. We present DeepMag, an end-to-end deep neural video-processing framework based on gradient ascent that enables automated magnification of subtle color and motion signals from a specific source, even in the presence of large motions of various velocities. The advantages of DeepMag are highlighted via the task of video-based physiological visualization. Through systematic quantitative and qualitative evaluation of the approach on videos with different levels of head motion, we compare the magnification of pulse and respiration to existing state-of-the-art methods. Our method produces magnified videos with substantially fewer artifacts and blurring whilst magnifying the physiological changes by a similar degree.

中文翻译：

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深磁

许多重要的物理现象都涉及难以用肉眼观察到的细微信号，但将它们可视化可以提供非常丰富的信息。当前的运动放大技术可以揭示视频中这些小的时间变化，但需要关于目标信号的精确先验知识，并且不能处理类似频率的干扰运动。我们提出了 DeepMag，这是一种基于梯度上升的端到端深度神经视频处理框架，即使在存在各种速度的大运动的情况下，也可以自动放大来自特定来源的细微颜色和运动信号。通过基于视频的生理可视化任务突出了 DeepMag 的优势。通过对具有不同头部运动水平的视频的方法进行系统的定量和定性评估，我们将脉冲和呼吸的放大率与现有的最先进方法进行比较。我们的方法产生的放大视频具有更少的伪影和模糊，同时将生理变化放大了相似的程度。