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Cited by 786

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Image smoothing via L0gradient minimization

Xu, Li, Lu, Cewu, Xu, Yi, Jia, Jiaya·2011·ACM Transactions on Graphics

Abstract

We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.

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Crossref · 10.1145/2070781.2024208

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Metadata

Authors
Xu, L., Lu, C., Xu, Y., & Jia, J.
Venue
ACM Transactions on Graphics
Published
2011-12
Volume
30
Issue
6
Pages
1-12
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2996868574
Cited by
786
References
39
FWCI
28.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Image smoothing via L0gradient minimization. https://app.cochat.ai/papers/10.1145/2070781.2024208
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Xu, L., Lu, C., Xu, Y., & Jia, J. (2011). Image smoothing via L0gradient minimization. ACM Transactions on Graphics. https://doi.org/10.1145/2070781.2024208

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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.