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Cited by 526

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Image completion with structure propagation

Sun, Jian, Yuan, Lu, Jia, Jiaya, Shum, Heung-Yeung·2005·ACM Transactions on Graphics

Abstract

In this paper, we introduce a novel approach to image completion, which we call structure propagation. In our system, the user manually specifies important missing structure information by extending a few curves or line segments from the known to the unknown regions. Our approach synthesizes image patches along these user-specified curves in the unknown region using patches selected around the curves in the known region. Structure propagation is formulated as a global optimization problem by enforcing structure and consistency constraints. If only a single curve is specified, structure propagation is solved using Dynamic Programming. When multiple intersecting curves are specified, we adopt the Belief Propagation algorithm to find the optimal patches. After completing structure propagation, we fill in the remaining unknown regions using patch-based texture synthesis. We show that our approach works well on a number of examples that are challenging to state-of-the-art techniques.

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Metadata

Authors
Sun, J., Yuan, L., Jia, J., & Shum, H.-Y.
Venue
ACM Transactions on Graphics
Published
2005-07
Volume
24
Issue
3
Pages
861-868
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2147901574
Cited by
526
References
1
FWCI
36.30
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Image completion with structure propagation. https://app.cochat.ai/papers/10.1145/1073204.1073274
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Sun, J., Yuan, L., Jia, J., & Shum, H.-Y. (2005). Image completion with structure propagation. ACM Transactions on Graphics. https://doi.org/10.1145/1073204.1073274

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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