MATLAB implementation of compressive sensing example as described in R.
Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118],
July 2007. The code acquires 250 averaged random measurements of a 2500
pixel image. We assume that the image has a sparse representation in the
DCT domain (not very sparse in practice). Hence the image can be
recovered from its compressed form using basis pursuit.