  We present a new neural network approach for deblending galaxy images in
astronomical data using Residual Dense Neural network (RDN) architecture. We
train the network on synthetic galaxy images similar to the typical
arrangements of field galaxies with a finite point spread function (PSF) and
realistic noise levels. The main novelty of our approach is the usage of two
distinct neural networks: i) a deblending network which isolates a single
galaxy postage stamp from the composite and, ii) a classifier network which
counts the remaining number of galaxies. The deblending proceeds by iteratively
peeling one galaxy at a time from the composite until the image contains no
further objects as determined by the classifier, or by other stopping criteria.
By looking at the consistency in the outputs of the two networks, we can assess
the quality of the deblending. We characterize the flux and shape
reconstructions in different quality bins and compare our deblender with the
industry standard, SExtractor. We also discuss possible future extensions for
the project with variable PSFs and noise levels.
