  With the onset of large-scale astronomical surveys capturing millions of
images, there is an increasing need to develop fast and accurate deconvolution
algorithms that generalize well to different images. A powerful and accessible
deconvolution method would allow for the reconstruction of a cleaner estimation
of the sky. The deconvolved images would be helpful to perform photometric
measurements to help make progress in the fields of galaxy formation and
evolution. We propose a new deconvolution method based on the Learnlet
transform. Eventually, we investigate and compare the performance of different
Unet architectures and Learnlet for image deconvolution in the astrophysical
domain by following a two-step approach: a Tikhonov deconvolution with a
closed-form solution, followed by post-processing with a neural network. To
generate our training dataset, we extract HST cutouts from the CANDELS survey
in the F606W filter (V-band) and corrupt these images to simulate their
blurred-noisy versions. Our numerical results based on these simulations show a
detailed comparison between the considered methods for different noise levels.
