  This paper presents a general framework to build fast and accurate algorithms
for video enhancement tasks such as super-resolution, deblurring, and
denoising. Essential to our framework is the realization that the accuracy,
rather than the density, of pixel flows is what is required for high-quality
video enhancement. Most of prior works take the opposite approach: they
estimate dense (per-pixel)-but generally less robust-flows, mostly using
computationally costly algorithms. Instead, we propose a lightweight flow
estimation algorithm; it fuses the sparse point cloud data and (even sparser
and less reliable) IMU data available in modern autonomous agents to estimate
the flow information. Building on top of the flow estimation, we demonstrate a
general framework that integrates the flows in a plug-and-play fashion with
different task-specific layers. Algorithms built in our framework achieve 1.78x
- 187.41x speedup while providing a 0.42 dB - 6.70 dB quality improvement over
competing methods.
