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I'm not claiming the demo is fake, I'm just wondering why the video looks so strange.
Forensic police drama writers everywhere: vindicated. This is seriously cool technology.
not really, because it's not the same thing what CSI does it add information that wasn't initially there whereas what this is doing is just unscrambling the information. This is only for photos where the camera has moved during exposure, so should be great for low light shots (indoors etc) where you need a shutter speed a bit slower than optimal, say 1/5 second
Earlier quoted context omitted.
Rainn Wilson is actually a really smart guy. He was playing the classic stupid guy being wowed by a genius card, it was an actor's way of complimenting him.
Anti-intellectualism aside, they still interrupted an amazing talk without adding any value. An actor's way of dominating the stage even when it's not your turn. A bit rude and unnecessary.
Earlier quoted context omitted.
Anti-intellectualism aside, they still interrupted an amazing talk without adding any value. An actor's way of dominating the stage even when it's not your turn. A bit rude and unnecessary.
He was paid by the company to interject with quips during the demos, so any rudeness is understood by both parties. An awkward situation for all involved.
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…
I'm no expert in this so correct me if I'm wrong, but isn't a blur convolution only reversible ambiguously? You still have to perform smart guesses how to undo the average, right?
So, why did the whole video look really fake? It seemed to bob around in a very predictable manner. When the first sharpening took effect, it panned and zoomed exactly in time with the appearance of the second image. I'm not claiming the demo is fake, I'm just wondering why the video looks so strange.
So, why did the whole video look really fake? It seemed to bob around in a very predictable manner. When the first sharpening took effect, it panned and zoomed exactly in time with the appearance of the second image. I'm not claiming the demo is fake, I'm just wondering why the video looks so strange.
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…
Does this algorithm lend itself to video? 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…
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...
Earlier quoted context omitted.
Rainn Wilson is actually a really smart guy. He was playing the classic stupid guy being wowed by a genius card, it was an actor's way of complimenting him.
Anti-intellectualism aside, they still interrupted an amazing talk without adding any value. An actor's way of dominating the stage even when it's not your turn. A bit rude and unnecessary.
I'm more impressed with that overhead display - seems impossible? 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 ?!
As for "massive resolution", slicing up a framebuffer and shooting out the components to multiple projectors wouldn't be a new idea, and I'll bet that's what was done here.