  We present an implementation of a blind source separation algorithm to remove
foregrounds off millimeter surveys made by single-channel instruments. In order
to make possible such a decomposition over single-wavelength data: we generate
levels of artificial redundancy, then perform a blind decomposition, calibrate
the resulting maps, and lastly measure physical information. We simulate the
reduction pipeline using mock data: atmospheric fluctuations, extended
astrophysical foregrounds, and point-like sources, but we apply the same
methodology to the AzTEC/ASTE survey of the Great Observatories Origins Deep
Survey-South (GOODS-S). In both applications, our technique robustly decomposes
redundant maps into their underlying components, reducing flux bias, improving
signal-to-noise, and minimizing information loss. In particular, the GOODS-S
survey is decomposed into four independent physical components, one of them is
the already known map of point sources, two are atmospheric and systematic
foregrounds, and the fourth component is an extended emission that can be
interpreted as the confusion background of faint sources.
