One of the things I did during my stint at Sony was video stabilization for their desktop nonlinear video editing software. We were hoping to not only stabilize (which is a pretty straightforward thing to do these days) but also use the motion data to inform a deconvolution to clean up motion blurred frames. Alas, my project ran out of time and we ended up licensing a third party stabilizer.

But yes, most image processing techniques like this lend themselves to video. This is something I hope my startup will someday be able to integrate into our collaborative video editor...

I should clarify: many things like this lend themselves to video, but not directly. As ibuildthings pointed out you have to be very careful becuase for video the human eye is very sensitive to the sharp edge artifacts algorithms like this can produce; much more so than it is to motion blur.

Yep, I can imagine continuity problems, flicker etc. could show up in video.

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.

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...

Actually human eye/brain is much more forgiving to motion blur in video that in still images. The chances are that one will spot quite a number of artifacts [compression as well as blur] in a grabbed frame of an HD quality video, which is otherwise unacceptable in a photograph.

In theory, you can tweak this method such that it is run in parallel frames 'almost' independently. I say 'almost' since you will have to factor in temporal coherence, such that deconvolution kernels do not vary much between subsequent frames (otherwise it will cause unwarranted flicker and jitter like effects)

However, more pertinent and related problem with respect to video is that of image stabilization. And computer vision community is making some exciting strides in the front; for eg: http://web.cecs.pdx.edu/~fliu/ .

For a while digital camcorders has had a digital image stabilisation feature build in. It seems to work like this.

http://camcorders.about.com/od/camcorders101/a/optical_vs_di...