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Adobe Demos Photo Unblurring At MAX 2011
karthikmns writes with word of an amazing demo presented last week at Adobe's annual MAX convention. You'll have to watch the video, but the enthusiastic crowd reaction seems genuine (or at least justified), even in an audience full of Photoshop enthusiasts, as photographs are algorithmically deblurred. (Maybe in the future, cameras will keep records of their own motion in metadata to assist such software efforts, rather than relying on in-built anti-shake software.) No word about when this will turn up for consumers in anything besides demo form, but I suspect similar software's already in use at Ft. Meade and Langley.
I'd be able to see the demo!
Did you watch the video? It makes unreadable text readable. That falls into the category of making missing data suddenly appear.
This demo came during the 'Sneak Peaks' portion of the conference. The technology may never make it to market.
That being said, I was at MAX and the demo was as amazing as it looks. Essentially, the software determines the motion/jitter of the camera at the time the photo was taken (i.e. figures out what caused the blur) and then undoes it. I can't imagine why they wouldn't include this in future version of photoshop.
How hard can it be, I mean, they've been doing it in movies since at least the 80s. Hell even the $500 dell desktop on CSI:miami can do it.
"we've got a convenience store video feed of the getaway car, the camera was recording in 480i from 300 yards away"
"can you sharpen it up a little?"
"sure. one moment... ok got it. License plate is california JGL-711. Ok just a bit more... yeah, looks like registration expires march 2012. Wait, let me clean it up some more, yeah it looks like there's a small identifying scratch on the trunk lid about a half inch long shaped like a boomerang. Oh wait, this is the new version of the software, let me zoom in a bit further, yeah I'm pretty sure I'm seeing loose skin cells on the edge of the trunk lid, maybe our missing person is in the trunk!"
"good work, now where's my sunglasses?"
yeeeeeeeeeeeeeeeeeeeeeeeeeeeaaaaahhhh!
I think it would be better to say that [most of] the data are already present; the data just happen to be initially in an unwanted form.
This does NOT fix images that are out of focus. This fixes motion blur. The two are entirely unrelated.
Take off every sig. For great justice.
No, and really no to everyone else. This is making _obfuscated_ data suddenly because visible.
It characterizes the the motion of the camera from the blur then reverses it: essentially an image stabilization algorithm. It's like making voices audible over loud music by figuring out what the song is and subtracting it from the mix.
It's cool, but not magic. They aren't even pretending to add in missing data like a CSI zoom. Nor does it even seem to take care of simple out of focus situations. So let's not get too excited, well, unless you've got a cheap/slow camera.
Of course. You can't get back more information than that is on the picture. But for a photograph it's enough that it looks good.
Wich reminds me another similar algorithm that worked on human faces. It could restore very lowres images to a sharp, almost perfect face. It's just that face was completely different from the one on the original picture.
It's cool, but not magic.
Right. I did exactly this with at least one ring image from Voyager 1's encounter with Saturn, and that was in 1980 (although I think I didn't get around to writing the code and actually de-blurring the image for two or three years after it was taken). I believe we used a VAX 11/730 to perform the computations.
FYI, Voyager pictures were 800x800 pixels, taken in monochrome with a filter applied in front of the camera. I don't recall whether this particular picture was a single image or a colour image taken with three filters. If the latter then there would have been an interesting twist: the three images would have been taken 48 seconds apart, so the spacecraft would have moved detectably from one colour to the next, so some semi-clever stuff would have been necessary to deblur three individual images and then merge them. But I honestly don't remember after all this time whether we had to do that.
This does NOT fix images that are out of focus. This fixes motion blur. The two are entirely unrelated.
Except that both are examples of convolution and deconvolution. In motion blur, the convolution kernel resembles a straight line in the direction of motion. In unfocused images, the kernel has circular symmetry. I used to write simple deconvolution algorithms about 10 years ago, but only for motion blur, where the kernel was easy to find from the conditions in a well-defined industrial setting. Unfocused images are harder to deal with, because the convolution kernel goes to zero at certain intervals, so information is destroyed.
As mentioned in my other post, here are some examples of more sophisticated image reconstruction from many years ago. When the kernel is unknown, the image can still be reconstructed using statistical techniques (basically because the kernel is the same for all points in the image).
Escher was the first MC and Giger invented the HR department.
Yeah, this is standard math. A completely out-of-focus picture actually contains nearly as much information as a sharp photo, it's just smeared with a reversible mathematical transform called a point spread function. Reverse it and you get the in-focus image back. There have been third party programs to do this for about a decade. The main problems have been processing speed (it could take a half hour or more a decade ago), determining the point spread function (you have both focus and camera shake, and the former can make figuring out the latter really hard), lens/sensor defects and image format compression (the PSF you calculate for a local region may not work well for the entire picture), and boundary conditions.
Unblurring without having any additional information has been done by academic software before using a process called blind deconvolution, look it up for some interesting pictures and videos.
It's a rather "expensive" (cpu-intensive) operation, and indeed having sensor data about how the camera has shaken during exposure would significantly help in restoring the image. Interestingly, even cheap smart phones with crappy cameras will often already have movement sensor on-board, so there are some possibilities to improve image quality right after taking a picture; all it takes is a bit of software. How long until someone here whips up an improved Android camera app?
I'm probably under-informed, but I haven't heard of any cameras with full-blown movement sensor, although I know some of them can work out portrait vs landscape by now. Sounds like camera manufacturers have some catching up to do in the hardware department.
Visit http://ringbreak.dnd.utwente.nl/~mrjb/growingbettersoftware to download your free copy of the book
This uses a single image as input, and tries to determine a local prior (L) and a motion kernel (f). It switches between optimization of each in turn, and produces results similar to the demo seen in the video. Given that Aseem works for Adobe, I suspect this work is now close to release.
Cheers,
Toby Haynes
Anything I post is strictly my own thoughts and doesn't necessarily have anything to do with the opinions of IBM.