  Image inpainting is a restoration method that reconstructs missing image
parts. However, a carefully selected mask of known pixels that yield a high
quality inpainting can also act as a sparse image representation. This
challenging spatial optimisation problem is essential for practical
applications such as compression. So far, it has been almost exclusively
adressed by model-based approaches. First attempts with neural networks seem
promising, but are tailored towards specific inpainting operators or require
postprocessing.
  To address this issue, we propose the first generative adversarial network
(GAN) for spatial inpainting data optimisation. In contrast to previous
approaches, it allows joint training of an inpainting generator and a
corresponding mask optimisation network. With a Wasserstein distance, we ensure
that our inpainting results accurately reflect the statistics of natural
images. This yields significant improvements in visual quality and speed over
conventional stochastic models. It also outperforms current spatial
optimisation networks.
