论文标题
从编码曝光技术中恢复压缩视频的统一框架
A Unified Framework for Compressive Video Recovery from Coded Exposure Techniques
论文作者
论文摘要
已经提出了几种用于以低带宽获取高帧速率视频的编码曝光技术。最近，已经提出了一台编码2杆摄像机，该摄像头可以在一次曝光中获得两个压缩测量，这与先前建议的编码曝光技术不同，该技术只能获得一个测量。尽管对于有效的视频恢复而言，两个测量值比一个测量要好，但我们仍未意识到两个测量的明显优势，无论是定量还是定性上。在这里，我们提出了一个基于统一的学习框架，以在仅捕获单个编码图像（Flutter快门，像素编码的曝光）与每次暴露（C2B）两次测量的框架（C2B）之间进行定性和定量的比较。我们基于学习的框架包括一个变化的卷积层，然后是完全卷积的深神经网络。我们提出的统一框架在所有三种传感技术中都实现了艺术的重建状态。进一步的分析表明，当大多数场景点静态时，C2B传感器比获得单个像素的编码测量具有显着优势。但是，当大多数场景都进行运动时，C2B传感器对单像素的编码曝光测量只有边缘益处。
Several coded exposure techniques have been proposed for acquiring high frame rate videos at low bandwidth. Most recently, a Coded-2-Bucket camera has been proposed that can acquire two compressed measurements in a single exposure, unlike previously proposed coded exposure techniques, which can acquire only a single measurement. Although two measurements are better than one for an effective video recovery, we are yet unaware of the clear advantage of two measurements, either quantitatively or qualitatively. Here, we propose a unified learning-based framework to make such a qualitative and quantitative comparison between those which capture only a single coded image (Flutter Shutter, Pixel-wise coded exposure) and those that capture two measurements per exposure (C2B). Our learning-based framework consists of a shift-variant convolutional layer followed by a fully convolutional deep neural network. Our proposed unified framework achieves the state of the art reconstructions in all three sensing techniques. Further analysis shows that when most scene points are static, the C2B sensor has a significant advantage over acquiring a single pixel-wise coded measurement. However, when most scene points undergo motion, the C2B sensor has only a marginal benefit over the single pixel-wise coded exposure measurement.
