Image Inpainting Using Wasserstein Generative Adversarial Imputation Network

@inproceedings{Vasata2021ImageIU,
  title={Image Inpainting Using Wasserstein Generative Adversarial Imputation Network},
  author={Daniel Vasata and Tom{\'a}s Halama and Magda Friedjungov{\'a}},
  booktitle={International Conference on Artificial Neural Networks},
  year={2021}
}
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image. The aim of this paper is to introduce an image inpainting model based on Wasserstein Generative Adversarial Imputation Network. The generator network of the model uses building blocks of convolutional layers with different dilation rates, together with skip connections that help the model reproduce fine details of the output. This combination yields a universal… 
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By emulating the complete inPainting pipeline with two networks for mask generation and neural surrogate inpainting, this work obtains a model for highly adaptive adaptive mask generation that can achieve competitive quality with an acceleration by as much as four orders of magnitude.

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Progressive feature generation with mask awareness for image inpainting

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