  Deep image prior (DIP) was recently introduced as an effective unsupervised
approach for image restoration tasks. DIP represents the image to be recovered
as the output of a deep convolutional neural network, and learns the network's
parameters such that the output matches the corrupted observation. Despite its
impressive reconstructive properties, the approach is slow when compared to
supervisedly learned, or traditional reconstruction techniques. To address the
computational challenge, we bestow DIP with a two-stage learning paradigm: (i)
perform a supervised pretraining of the network on a simulated dataset; (ii)
fine-tune the network's parameters to adapt to the target reconstruction task.
We provide a thorough empirical analysis to shed insights into the impacts of
pretraining in the context of image reconstruction. We showcase that
pretraining considerably speeds up and stabilizes the subsequent reconstruction
task from real-measured 2D and 3D micro computed tomography data of biological
specimens. The code and additional experimental materials are available at
https://educateddip.github.io/docs.educated_deep_image_prior/.
