21–30 of 130 posts
How does it disappear at the end - or is that a virtual digital overlay?
Wait, is the entire background rear projected, like a borderless movie theater screen? Must be massive resolution ?!
Let me load the specially constructed set of parameters specific to this image so that when I do the next step you get a really clear image. That was a little too hand-wavy. I'm a little dubious until I see what went into that phase.
He used the same parameter profile each time.
It's nothing new really, but algorithms for it have advanced tremendously. For example, there's some results from 2009 http://www.youtube.com/watch?v=uqMW3OleLM4
Teuobk on HN also made a startup/app based on this, but it seems to be down now: http://news.ycombinator.com/item?id=2460887
The random anti-intellectual comments from the guys in the wheely chairs were extremely annoying and unfunny. This guy is there, showing something truly amazing, and they're all "What's an algorithm? Haha!". And they'll get away with it too.
I suppose this is more of image sharpening rather than reconstruction. Is this very different from technology on cameras/phones that tries to reduce of photo blurness due to unsteady hands?
What makes you suppose that? On an abstract level, you can model blur and camera shake with a convolution kernel. You can then invert the kernel and get back the original image. As an analogy, imagine that someone gives you an audio file with an echo. You can subtract the echo with a filter. Camera shake is harder because of the extra dimension. (Of course, you only get back the exact original in the world of mathema…
Let me load the specially constructed set of parameters specific to this image so that when I do the next step you get a really clear image. That was a little too hand-wavy. I'm a little dubious until I see what went into that phase.
The parameter preload isn't cheating -- if they're anything like the InFocus params, they're pretty obvious but somewhat tedious. They're things like telling it that you're trying to correct motion blur rather than focus blur, what level of artifacting you're willing to put up with (for forensics or text recovery, you can put up with a lot of noise in the uninteresting part of the picture), the desired hardness of recovered edges, that sort of thing. It would have just been a time-waster for the demo (and, like in the demo, InFocus allows you to save the params as a preset).
It's called blind deconvolution. Blind means that they have to first estimate the original convolution/blur kernel and in the second phase, apply the deconvolution. If there's acceleration sensor on the camera, you can use data from that for the blur kernel. It's nothing new really, but algorithms for it have advanced tremendously. For example, there's some results from 2009 http://www.youtube.com/watch?v=uqMW3OleLM4…
Probably would take a massive cloud of systems to correct any highdef video signal, but it would be impressive for many applications (news broadcast, sports, or any live event, security, or remote robots)
Granted to achieve performance on the order of near realtime dsp, it would require an impressive hardware system. Then again, when I can spend the price of a coffee and get access to a cloud of cpu's...