Richard Baraniuk in his presentation entitled Multiscale Geometric Analysis (the real player version of this presentation is here) makes a very good presentation of how compressed sensing works. What is interesting in this presentation are the questions of the audience at the end: They focus on the engineering of the mask/camera but in fact the new element is the mathematics (and that element is not a new wavelet basis). The progressivity aspect of the reconstruction is important and it is, in my view, shown very well in the recent bayesian paper on this. So in order to build a new camera ones needs a DMD controlled board (at 10K$), one pixel and you're set. The underlying reason why this concept is so important is when it brings large advantage over current solutions. For instance, movement detection. In traditional approach one needs to do some type of optic flow computation between two frames to evaluate changes.
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