  This work describes our winning solution for the Chalearn LAP In-painting
Competition Track 3 - Fingerprint Denoising and In-painting. The objective of
this competition is to reduce noise, remove the background pattern and replace
missing parts of fingerprint images in order to simplify the verification made
by humans or third-party software. In this paper, we use a U-Net like CNN model
that performs all those steps end-to-end after being trained on the competition
data in a fully supervised way. This architecture and training procedure
achieved the best results on all three metrics of the competition.
