MISF:Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

We proposed a novel approach for high-fidelity image inpainting. Specifically, we use a single predictive network to conduct predictive filtering at the image level and deep feature level, simultaneously. The image-level filtering is to recover details, while the deep feature-level filtering is to complete semantic information, which leads to high-fidelity inpainting results. Our method outperforms state-of-the-art methods on three public datasets.[ArXiv]

Dataset

For data folder path (CelebA) organize them as following:

--CelebA
   --train
      --1-1.png
   --valid
      --1-1.png
   --test
      --1-1.png
   --mask-train
	  --1-1.png
   --mask-valid
      --1-1.png
   --mask-test
      --0%-20%
        --1-1.png
      --20%-40%
        --1-1.png
      --40%-60%
        --1-1.png