The GAN model for image inpainting consists of two generator networks (a coarse generator and a refinement generator) and two discriminator networks (a local discriminator and a global discriminator), as shown here:
GAN model for image inpainting: Image x represents the input image; x1 and x2 represent generated images by coarse and refinement generators, respectively; xr represents the original complete image; and m represents the mask for missing part in the image.
The generator model uses two-stage coarse-to-fine architecture. The coarse generator is a 17-layer encoder-decoder CNN and dilated convolutions ...
