  Unbiased sky background modeling is crucial for the analysis of deep
wide-field images, but it remains a major challenge in low surface brightness
astronomy. Traditional image processing algorithms are often designed to
produce artificially flat backgrounds, erasing astrophysically meaningful
structures. In this paper, we present three ideas that can be combined to
produce wide-field astronomical data that preserve accurate representations of
the background sky: (1) Use of all-sky infrared/sub-mm data to remove the
large-scale time-varying components while leaving the scattered light from
Galactic cirrus intact, with the assumptions of (a) the underlying background
has little power on small scales, and (b) the Galactic cirrus in the field is
optically thin on large scales; (2) Censoring of frames contaminated by
anomalously prominent wings in the wide-angle point-spread function; and (3)
Incorporation of spatial covariance in image stacking that controls the local
background consistency. We demonstrate these methods using example datasets
obtained with the Dragonfly Telephoto Array, but these general techniques are
prospective to be applied to improve sky models in data obtained from other
wide-field imaging surveys, including those from the upcoming Vera Rubin
Telescope.
