Fig. 7

An illustration of the relationship between CS and MFA. The sensing matrix projects the new measured data along the axes. The measurements are missing a component and the job of CS inversion is to recover the missing component. In higher dimensions, the data is projected onto a subspace; several components are missing, and several components are available. In the experiments section, 4×4 patches are used and half of the pixels are blocked, so eight components are available and eight must be recovered for every patch. The CS inversion procedure maps the measurements back to the manifold using the previously learned MFA that approximates the manifold with ellipsoids