3 ms·
For a much better explanation of this stuff from Terry Tao (mentioned in the article as being responsible for much of the theoretical basis of the technique; Fi
by gjm11 17y ago
For a much better explanation of this stuff from Terry Tao (mentioned in the article as being responsible for much of the theoretical basis of the technique; Fields medallist; very very smart chap) see http://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ http://terrytao.wordpress.com/2007/04/13/compressed-sensing-... .
One important way in which I think the Wired piece is Just Plain Wrong: you don't sample a small fraction of the individual image pixels. You take a small number of samples, each of which is a kinda-random combination of image pixels.
Some examples and further quite-friendly explanation here: http://www.acm.caltech.edu/l1magic/examples.html http://www.acm.caltech.edu/l1magic/examples.html .
More from Tao: http://terrytao.wordpress.com/2008/12/05/the-uniform-uncertainty-principle-and-compressed-sensing/ http://terrytao.wordpress.com/2008/12/05/the-uniform-uncerta... and http://terrytao.wordpress.com/2009/05/25/reflections-on-compressed-sensing/ http://terrytao.wordpress.com/2009/05/25/reflections-on-comp... both of which are mostly links to more detailed things, well worth reading if you don't mind mathematics.
- _delirium 17y agoThanks for the link; that made a lot more sense. The way Wired described it made it sound like the L1 minimization was almost irrelevant. The closest to a pseudocode I could get out of their article was something like: 1) apply an L1 minimization to do a first-cut noise reduction on some random noisy data; 2) apply an iterative algorithm to the result until convergence. That made it sound like all the magic was in the invention of some crazy algorithm for #2, which isn't really the case.