  Recent GAN-based (Generative adversarial networks) inpainting methods show
remarkable improvements and generate plausible images using multi-stage
networks or Contextual Attention Modules (CAM). However, these techniques
increase the model complexity limiting their application in low-resource
environments. Furthermore, they fail in generating high-resolution images with
realistic texture details due to the GAN stability problem. Motivated by these
observations, we propose a multi-GAN architecture improving both the
performance and rendering efficiency. Our training schema optimizes the
parameters of four progressive efficient generators and discriminators in an
end-to-end manner. Filling in low-resolution images is less challenging for
GANs due to the small dimensional space. Meanwhile, it guides higher resolution
generators to learn the global structure consistency of the image. To constrain
the inpainting task and ensure fine-grained textures, we adopt an LBP-based
loss function to minimize the difference between the generated and the ground
truth textures. We conduct our experiments on Places2 and CelebHQ datasets.
Qualitative and quantitative results show that the proposed method not only
performs favorably against state-of-the-art algorithms but also speeds up the
inference time.
