  Forthcoming instruments designed for high-cadence large-area surveys, such as
the Dark Energy Survey and Large Synoptic Survey Telescope, will generate
several GB of data products every few minutes during survey operations. Since
such surveys are designed to operate with minimal observer interaction,
automated real-time analysis of these large images is necessary to ensure
uninterrupted production of science-quality data. We describe a software
infrastructure suite designed to support such surveys, focusing particularly on
ImageHealth, a tool for near-real-time processing of large images. These image
manipulation and analysis algorithms were applied to simulated data from the
Dark Energy Survey, as well as observed data collected by the Y4KCam on the
CTIO 1m telescope and the Mosaic camera on the Blanco telescope. The accuracy
and speed of the ImageHealth code in particular were benchmarked against
results from SourceExtractor, a standard image analysis tool ubiquitous in the
astronomical community. ImageHealth is shown to provide comparable accuracy to
SourceExtractor, but with significantly shorter execution time. Based on the
importance of real-time analysis in reaching the Dark Energy Survey's science
goals, ImageHealth and other aspects of this analysis package were incorporated
(in modified form) into the Survey Image System Process Integration, the Dark
Energy Camera software control environment. The original ImageHealth code,
however, is completely instrument-independent, and is freely available for use
within other observational data-taking environments.
