#1260 overall
Compressed Sensing (Terence Tao)
YouTube
1 recommender · 1 thread · recommended in 1 year, 2013 · score 0.7
A lecture series by mathematician Terence Tao introducing compressed sensing, the theory of recovering signals from far fewer measurements than traditional sampling would require, by exploiting sparsity. It includes image recovery examples.
One commenter calls it a very well presented lecture of about an hour total and points to its image recovery examples as a way to learn the topic.
Mathematicsintermediatefreevideo lectures#compressed sensing#sparsity#signal recovery#image reconstruction#tao
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Oh. I've tried finding that exact publication - they took a video of a city scene with dense fog/clouds in the background. If you'd play the video you couldn't see any shapes through the fog. At all. But after passing the whole video through reconstruction/de-noising, they've been able to recover the background (you could see s mountain, instead of dense fog).
It is not surprising and it's a pretty standard result actually. Can be called by different names (sparse sensing, denoising, superresolution, ...) and done by different methods (BP, LASSO, ...), but it always comes down to having a model (with some priors, e.g sparsity prior!) and using that model to re-project the data from your measurement projection into the projection of some interest to you.
I have no idea at what level you are, so I'd suggest this very well presented lecture (1 hour total): Compressed Sensing by Terence Tao http://www.youtube.com/playlist?list=PLC94A02A1218B24DF if you want to learn more. Examples of image recovery are in the part 5, part 6.