  This paper presents a general framework that simultaneously improves the
quality and the execution speed of a range of video enhancement tasks, such as
super-sampling, deblurring, and denoising. The key to our framework is a pixel
motion estimation algorithm that generates accurate motion from low-quality
videos while being computationally very lightweight. Our motion estimation
algorithm leverages point cloud information, which is readily available in
today's autonomous devices and will only become more common in the future. We
demonstrate a generic framework that leverages the motion information to guide
high-quality image reconstruction. Experiments show that our framework
consistently outperforms the state-of-the-art video enhancement algorithms
while improving the execution speed by an order of magnitude.
