  Direct imaging of exoplanets involves the extraction of very faint signals
from highly noisy data sets, with noise that often exhibits significant
spatial, spectral and temporal correlations. As a results, a large number of
post-processing algorithms have been developed in order to optimally
decorrelate the signal from the noise. In this paper, we explore four such
closely related algorithms, all of which depend heavily on the calculation of
covariances between large data sets of imaging data. We discuss the
similarities and differences between these methods, and demonstrate how the use
sequential calculation techniques can significantly improve their computational
efficiencies.
