Editing Super-resolution imaging (section)

==Technical implementations==
There are many both single-frame and multiple-frame variants of SR. Multiple-frame SR uses the sub-[[pixel shift]]s between multiple low resolution images of the same scene. It creates an improved resolution image fusing information from all low resolution images, and the created higher resolution images are better descriptions of the scene. Single-frame SR methods attempt to magnify the image without producing blur. These methods use other parts of the low resolution images, or other unrelated images, to guess what the high-resolution image should look like. Algorithms can also be divided by their domain: [[frequency domain|frequency]] or [[Digital signal processing#Time and space domains|space domain]]. Originally, super-resolution methods worked well only on grayscale images,<ref>P. Cheeseman, B. Kanefsky, R. Kraft, and J. Stutz, 1994</ref> but researchers have found methods to adapt them to color camera images.<ref name="users.soe.ucsc.edu"/> Recently, the use of super-resolution for 3D data has also been shown.<ref>S. Schuon, C. Theobalt, J. Davis, and S. Thrun,  [https://ai.stanford.edu/~schuon/2009/04/superresolution-of-3d-lidarboost.html "LidarBoost: Depth Superresolution for ToF 3D Shape Scanning"], In Proceedings of IEEE CVPR 2009</ref>