Super-resolving Herschel imaging: a proof of concept using Deep Neural
  Networks
  Wide-field sub-millimetre surveys have driven many major advances in galaxy
evolution in the past decade, but without extensive follow-up observations the
coarse angular resolution of these surveys limits the science exploitation.
This has driven the development of various analytical deconvolution methods. In
the last half a decade Generative Adversarial Networks have been used to
attempt deconvolutions on optical data. Here we present an autoencoder with a
novel loss function to overcome this problem in the sub-millimeter wavelength
range. This approach is successfully demonstrated on Herschel SPIRE 500$\mu$m
COSMOS data, with the super-resolving target being the JCMT SCUBA-2 450$\mu$m
observations of the same field. We reproduce the JCMT SCUBA-2 images with high
fidelity using this autoencoder. This is quantified through the point source
fluxes and positions, the completeness and the purity.
