  It is well-known that in inverse problems, end-to-end trained networks
overfit the degradation model seen in the training set, i.e., they do not
generalize to other types of degradations well. Recently, an approach to first
map images downsampled by unknown filters to bicubicly downsampled look-alike
images was proposed to successfully super-resolve such images. In this paper,
we show that any inverse problem can be formulated by first mapping the input
degraded images to an intermediate domain, and then training a second network
to form output images from these intermediate images. Furthermore, the best
intermediate domain may vary according to the task. Our experimental results
demonstrate that this two-stage domain-adapted training strategy does not only
achieve better results on a given class of unknown degradations but can also
generalize to other unseen classes of degradations better.
