  Compressed sensing enables the reconstruction of high-resolution signals from
under-sampled data. While compressive methods simplify data acquisition, they
require the solution of difficult recovery problems to make use of the
resulting measurements. This article presents a new sensing framework that
combines the advantages of both conventional and compressive sensing. Using the
proposed \stone transform, measurements can be reconstructed instantly at
Nyquist rates at any power-of-two resolution. The same data can then be
"enhanced" to higher resolutions using compressive methods that leverage
sparsity to "beat" the Nyquist limit. The availability of a fast direct
reconstruction enables compressive measurements to be processed on small
embedded devices. We demonstrate this by constructing a real-time compressive
video camera.
