  Facial image inpainting is a problem that is widely studied, and in recent
years the introduction of Generative Adversarial Networks, has led to
improvements in the field. Unfortunately some issues persists, in particular
when blending the missing pixels with the visible ones. We address the problem
by proposing a Wasserstein GAN combined with a new reverse mask operator,
namely Reverse Masking Network (R-MNet), a perceptual adversarial network for
image inpainting. The reverse mask operator transfers the reverse masked image
to the end of the encoder-decoder network leaving only valid pixels to be
inpainted. Additionally, we propose a new loss function computed in feature
space to target only valid pixels combined with adversarial training. These
then capture data distributions and generate images similar to those in the
training data with achieved realism (realistic and coherent) on the output
images. We evaluate our method on publicly available dataset, and compare with
state-of-the-art methods. We show that our method is able to generalize to
high-resolution inpainting task, and further show more realistic outputs that
are plausible to the human visual system when compared with the
state-of-the-art methods.
