The Convex Geometry of Linear Inverse Problems — mentioned by jey

Wed, 21 Mar 2018 03:06:16 GMT

Close, but not quite: compressed sensing really works in a lot of domains. You "just" have to know of a basis set in which your signal is sparse. This basis set can be (and often is) overcomplete; i.e. you don't need a minimal orthogonal set of basis functions. So if you know that "most pixels of an MRI are black" or "music only has a few non-zero Fourier frequencies", you can apply compressed sensing techniques to recover/reconstruct the underlying signal. This is a fairly mild and merely structural form of a priori side information, as opposed to having to know detailed precise prior…

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The Convex Geometry of Linear Inverse Problems · HackerPapers discussions

The Convex Geometry of Linear Inverse Problems — mentioned by jey