  We describe a new method to achieve point spread function (PSF) subtractions
for high- contrast imaging using Principal Component Analysis (PCA) that is
applicable to both point sources or extended objects (disks). Assuming a
library of reference PSFs, a Karhunen-Lo`eve transform of theses references is
used to create an orthogonal basis of eigenimages, on which the science target
is projected. For detection this approach provides comparable suppression to
the Locally Optimized Combination of Images (LOCI) algorithm, albeit with
increased robustness to the algorithm parameters and speed enhancement. For
characterization of detected sources the method enables forward modeling of
astrophysical sources. This alleviates the biases in the astrometry and
photometry of discovered faint sources, which are usually associated with LOCI-
based PSF subtractions schemes. We illustrate the algorithm performance using
archival Hubble Space Telescope (HST) images, but the approach may also be
considered for ground-based data acquired with Angular Differential Imaging
(ADI) or integral-field spectrographs (IFS).
