81–90 of 130 posts
So now we need blurring algorithms that cause actual information loss (I'm sure they already exist, but now there's suddenly a bigger market for them).
So now we need blurring algorithms that cause actual information loss (I'm sure they already exist, but now there's suddenly a bigger market for them).
We always did need those algorithms. There was a paper a few years ago on decoding gaussian-blurred documents to reveal the redacted passages. The only safe way is to completely remove the original pixels, e.g. by drawing a black box over the text.
Is the 'decoding' paper this one: http://dheera.net/projects/blur.php ? The blur function is just smearing pixel values across their neighbours in blocks, so you can treat it as a hashing function, and then generate enough candidates that eventually you get something that hashes correct (or close enough)
I'm not sure how practical it'd be on data longer than a credit-card number, but it's an interesting hack nevertheless.
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> So much of image (and audio!) processing is about getting away with noise the person can't detect :) Adobe's noise processing algorithms in their software are something else, especially in ACR6/Lightroom 3. I moved 'down' from a 5D2 to a Panasonic GF1 as I don't shoot pro anymore, and ISO1600 on this thing with a slightly-off exposure can be pretty noisy. Lightroom cleans it up incredibly well without losing clarit…
It's a very different kind of noise. Artifacts, is what he meant to write. Motion deconvolution done wrong tends to lead to things like ring artifacts and other things that look like the image has been really badly and inaccurately oversharpened. The basic theory for doing this type of deblurring isn't too bad, but making it easy and automatic becomes a really difficult computer vision-related problem. Adobe has been…
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…
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He was paid to be rude to the people who were paying him to be rude? That might not make it not rude, but I can't imagine anything that would come closer.
It was rude to be rude to the people paying him to be rude because it appears to the other members of the audience who were not being paid to be rude that being rude and interrupting the talk is acceptable practice.
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Either way, there's no point in watching a tech preview and being cynical about it.
I think there is. I much prefer cynicism to the blind adulation of "this tech is amazing and magical". Otherwise it's almost marketing spin.
What they should do is partner with a DSLR maker and put this in the firmware of the camera itself. Imagine one button blur correction. That'd be amazing.
I don't think DSLRs have the processing power needed to unblur the images. It took several seconds to unblur a section of an image. It'd probably be faster to upload the image to a cloud processor and have it unblur it for you.
This is a perfect "web service" where you can profit from selling the device to have free access to the service.
If you are wondering how they might be doing it, here is one approach that I saw in a computer vision class (no idea if they are doing anything similar to this) (slides: http://cs.nyu.edu/~fergus/presentations/fergus_deblurring.pd... (~60 MB ppt) paper: http://cs.nyu.edu/~fergus/papers/deblur_fergus.pdf (~10 MB pdf) ) The basic idea is that you have an unknown original image and it is convolved with an unknown blurri…