Background derivation and image flattening: getimages
  Modern high-resolution images obtained with space observatories display
extremely strong intensity variations across images on all spatial scales.
Source extraction in such images with methods based on global thresholding may
bring unacceptably large numbers of spurious sources in bright areas while
failing to detect sources in low-background or low-noise areas. It would be
highly beneficial to subtract background and equalize the levels of small-scale
fluctuations in the images before extracting sources or filaments. This paper
describes getimages, a new method of background derivation and image
flattening. It is based on median filtering with sliding windows that
correspond to a range of spatial scales from the observational beam size up to
a maximum structure width $X_{\lambda}$. The latter is a single free parameter
of getimages that can be evaluated manually from the observed image
$\mathcal{I}_{\lambda}$. The median filtering algorithm provides a background
image $\tilde{\mathcal{B}}_{\lambda}$ for structures of all widths below
$X_{\lambda}$. The same median filtering procedure applied to an image of
standard deviations $\mathcal{D}_{\lambda}$ derived from a
background-subtracted image $\tilde{\mathcal{S}}_{\lambda}$ results in a
flattening image $\tilde{\mathcal{F}}_{\lambda}$. Finally, a flattened
detection image
$\mathcal{I}_{{\lambda}\mathrm{D}}{\,=\,}\tilde{\mathcal{S}}_{\lambda}{/}\tilde{\mathcal{F}}_{\lambda}$
is computed, whose standard deviations are uniform outside sources and
filaments. Detecting sources in such greatly simplified images results in much
cleaner extractions that are more complete and reliable. As a bonus, getimages
reduces various observational and map-making artifacts and equalizes noise
levels between independent tiles of mosaicked images.
