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.

本研究提出了一种基于逆掩码网络的视知觉对抗网络，用于面部图像修复，该方法结合了一个新的反向掩码操作符和特征空间的损失函数。与现有最先进方法相比，我们的方法在高分辨率图像修复任务中产生更逼真的输出，更符合人类视觉系统的认知。