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Cited by 396

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

StructureFlow: Image Inpainting via Structure-Aware Appearance Flow

Open access
Ren, Yurui, Yu, Xiaoming, Zhang, Ruonan, Li, Thomas H, Liu, Shan, Li, Ge·2019·2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Abstract

Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.

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Metadata

Authors
Ren, Y., Yu, X., Zhang, R., Li, T. H., Liu, S., & Li, G.
Venue
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2019-10
Pages
181-190
OpenAlex
W2981682056
Cited by
396
References
7
FWCI
17.12
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: StructureFlow: Image Inpainting via Structure-Aware Appearance Flow. https://app.cochat.ai/papers/10.1109/iccv.2019.00027
The paper
Ren, Y., Yu, X., Zhang, R., Li, T. H., Liu, S., & Li, G. (2019). StructureFlow: Image Inpainting via Structure-Aware Appearance Flow. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00027

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.