  A major issue in optical astronomical image analysis is the combined effect
of the instrument's point spread function (PSF) and the atmospheric seeing that
blurs images and changes their shape in a way that is band and
time-of-observation dependent. In this work we present a very simple neural
network based approach to non-blind image deconvolution that relies on feeding
a Convolutional Autoencoder (CAE) input images that have been preprocessed by
convolution with the corresponding PSF and its regularized inverse. Compared to
our previous work based on Deep Wiener Deconvolution, the new approach is
conceptually simpler and computationally much less intensive while achieving
only marginally worse results. In this work we also present a new approach for
dealing with limited input dynamic range of neural networks compared to the
dynamic range present in astronomical images.
