  Deep learning methods using convolutional neural networks (CNN) have been
successfully applied to virtually all imaging problems, and particularly in
image reconstruction tasks with ill-posed and complicated imaging models. In an
attempt to put upper bounds on the capability of baseline CNNs for solving
image-to-image problems we applied a widely used standard off-the-shelf network
architecture (U-Net) to the "inverse problem" of XOR decryption from noisy data
and show acceptable results.
