Without having looked into it too much (mostly just the video), there seem to be fairly strong parallels with the motion tracking/estimation that modern video codecs do.
As I understand it, their system is trying to estimate the causes of noise in the image (camera shake, badly focused or dirty lens, etc) and apply an inverse transformation to produce the original 'real' image.
They can't extract detail out of nowhere, so a lot of it will likely be heuristically driven, accounting for specific sources of error and applying adaptive transforms with some sort of 'looks like what we think it should look like' cost function.
Figuring out the cost function is probably one of the hardest parts here.
In terms of video, you've got more data to work with, which in theory means more constraints to your solution making it potentially more accurate, but I suspect it'd also be very easy to get bogged down in the quantity of data.
As I understand it, their system is trying to estimate the causes of noise in the image (camera shake, badly focused or dirty lens, etc) and apply an inverse transformation to produce the original 'real' image.
They can't extract detail out of nowhere, so a lot of it will likely be heuristically driven, accounting for specific sources of error and applying adaptive transforms with some sort of 'looks like what we think it should look like' cost function.
Figuring out the cost function is probably one of the hardest parts here.
In terms of video, you've got more data to work with, which in theory means more constraints to your solution making it potentially more accurate, but I suspect it'd also be very easy to get bogged down in the quantity of data.
Combining and merging data from multiple independent sources is a different problem entirely, probably more like synthetic aperture imaging: http://vision.ucsd.edu/kriegman-grp/research/synthetic_ap_tr...