  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.
