Abstract
Spatial Super Resolution (SR) aims to recover fine image details, smaller than a pixel size. Temporal SR aims to recover rapid dynamic events that occur faster than the video frame-rate, and are therefore invisible or seen incorrectly in the video sequence. Previous methods for Space-Time SR combined information from multiple video recordings of the same dynamic scene. In this paper we show how this can be done from a single video recording. Our approach is based on the observation that small space-time patches ('ST-patches', e.g., 5x5x3) of a single 'natural video', recur many times inside the same video sequence at multiple spatio-temporal scales. We statistically explore the degree of these ST-patch recurrences inside 'natural videos', and show that this is a very strong statistical phenomenon. Space-time SR is obtained by combining information from multiple ST-patches at sub-frame accuracy. We show how finding similar ST-patches can be done both efficiently (with a randomized-based search in space-time), and at sub-frame accuracy (despite severe motion aliasing). Our approach is particularly useful for temporal SR, resolving both severe motion aliasing and severe motion blur in complex 'natural videos'.
| Original language | English |
|---|---|
| Pages (from-to) | 60 |
| Number of pages | 8 |
| Journal | 2011 Ieee Conference On Computer Vision And Pattern Recognition (Cvpr) |
| State | Published - 2011 |
| Event | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Colorado Springs, CO Duration: 20 Jun 2011 → 25 Jun 2011 |