Image inpainting using Wasserstein Generative Adversarial Network

Hua, Peng; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

doi:10.1117/12.2320212

Cited by 3 publications

(2 citation statements)

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“…The difference is that we use Wasserstein GAN loss [2] to train our model, which can ensure our model's stable training. Also, Hua et al [48] use Wasserstein GAN to do the task of image inpainting. Different from their method, we use the original WGAN loss to train our model instead of WGAN-GP [49].…”

Section: Introductionmentioning

confidence: 99%

Image Inpainting Based on Generative Adversarial Networks

Jiang

Yang

et al. 2020

IEEE Access

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Image inpainting aims to fill missing regions of a damaged image with plausibly synthesized content. Existing methods for image inpainting either fill the missing regions by borrowing information from surrounding areas or generating semantically coherent content from region context. They often produce ambiguous or semantically incoherent content when the missing region is large or with complex structures. In this paper, we present an approach for image inpainting. The completion model based on our proposed algorithm contains one generator, one global discriminator, and one local discriminator. The generator is responsible for inpainting the missing area, the global discriminator aims evaluating whether the repair result has global consistency, and the local discriminator is responsible for identifying whether the repair area is correct. The architecture of the generator is an auto-encoder. We use the skip-connection in the generator to improve the prediction power of the model. Also, we use Wasserstein GAN loss to ensure the stability of training. Experiments on CelebA dataset and LFW dataset demonstrate that our proposed model can deal with large-scale missing pixels and generate realistic completion results. INDEX TERMS AutoEncoder, image inpainting, skip-connection, stable training, wasserstein GAN.

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Section: Introductionmentioning

confidence: 99%

Image Inpainting Based on Generative Adversarial Networks

Jiang

Yang

et al. 2020

IEEE Access

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“…in [4,11,12,13]. We also discuss the comparison to other state-of-the art methods [8,9,14,6,7,5] where possible.…”

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confidence: 99%

Image Inpainting Using Wasserstein Generative Adversarial Imputation Network

Vašata,

Halama,

Friedjungová

2021

Preprint

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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 imputation model that is able to handle various scenarios of missingness with sufficient quality. To show this experimentally, the model is simultaneously trained to deal with three scenarios given by missing pixels at random, missing various smaller square regions, and one missing square placed in the center of the image. It turns out that our model achieves high-quality inpainting results on all scenarios. Performance is evaluated using peak signal-to-noise ratio and structural similarity index on two real-world benchmark datasets, CelebA faces and Paris StreetView. The results of our model are compared to biharmonic imputation and to some of the other state-of-the-art image inpainting methods.

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