ML Anthology

Space-Time Super-Resolution from a Single Video

Oded Shahar, Alon Faktor, Michal Irani
CVPR 2011 pp. 3353-3360
doi:10.1109/CVPR.2011.5995360 /cvpr/2011/shahar2011cvpr-space/

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., 5×5×3) 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'.

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Text

Shahar et al. "Space-Time Super-Resolution from a Single Video." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995360

Markdown

[Shahar et al. "Space-Time Super-Resolution from a Single Video." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/shahar2011cvpr-space/) doi:10.1109/CVPR.2011.5995360

BibTeX

@inproceedings{shahar2011cvpr-space,
  title     = {{Space-Time Super-Resolution from a Single Video}},
  author    = {Shahar, Oded and Faktor, Alon and Irani, Michal},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {3353-3360},
  doi       = {10.1109/CVPR.2011.5995360},
  url       = {https://mlanthology.org/cvpr/2011/shahar2011cvpr-space/}
}