Mr. Jack Lee Profile

Jack Lee

at Peking Univ

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Proceedings Article | 12 October 2022 Paper

Generative image inpainting with residual texture prior and cross-layer contextual attention

Lei Li, Yuesheng Zhu

Proceedings Volume 12342, 123421J (2022) https://doi.org/10.1117/12.2645154

KEYWORDS: Computer programming, Volume rendering, Convolution, Binary data, Visualization, Radon, Image restoration, Nickel, Performance modeling, Data modeling

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Existing image inpainting methods have shown promising performance in filling the missing regions with visually plausible contents. However, these methods tend to produce distorted structure and blurry texture. To address these issues, in this paper we propose a two-stage inpainting network that combines texture generation and image completion. In the first stage, a texture generator is used to hallucinate texture of the missing regions to guide the reconstruction in the next stage. In the second stage, considering the texture prior would gradually lose its guiding role with the deepening of the network, we adopt residual texture prior to generate fine details. We also introduce a cross-layer contextual attention module which can not only learn contextual attention in decoder feature map, but also benefit from the similar feature shifted from the encoder, generating reasonable structure and realistic texture. Our comparison results of both qualitative analysis and quantitative experiments on Paris StreetView and CelebA datasets demonstrate our proposed method has better inpainting performance than existing methods.

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