  The leading difficulty in achieving the contrast necessary to directly image
exoplanets and associated structures (eg. protoplanetary disks) at wavelengths
ranging from the visible to the infrared are quasi-static speckles, and they
are hard to distinguish from planets at the necessary level of precision. The
source of the quasi-static speckles is hardware aberrations that are not
compensated by the adaptive optics system. These aberrations are called
non-common path aberrations (NCPA). In 2013, Frazin showed how, in principle,
simultaneous millisecond (ms) telemetry from the wavefront sensor (WFS) and the
science camera behind a stellar coronagraph can be used as input into a
regression scheme that simultaneously and self-consistently estimates the NCPA
and the sought-after image of the planetary system (the exoplanet image). The
physical principle underlying the regression method is rather simple: the
wavefronts, which are measured by the WFS, modulate the speckles caused by the
NCPA and therefore can be used as probes of the optical system. The most
important departure from realism in the author's 2013 article was the
assumption that the WFS made error-free measurements. The simulations in Part I
provide results on the joint regression on the NCPA and the exoplanet image
from three different methods, called the ideal, the naive, and the
bias-corrected estimators. The ideal estimator is not physically realizable but
is a useful as a benchmark for simulation studies, but the other two are, at
least in principle. This article provides the regression equations for all
three of these estimators as well as a supporting technical discussion.
Briefly, the naive estimator simply uses the noisy WFS measurements without any
attempt to account for the errors, and the bias-corrected estimator uses
statistical knowledge of the wavefronts to treat errors in the WFS
measurements.
