Improving Complex Moiré Removal with Generative Supervision
The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing datasets to encompass the complex moiré patterns captured in uncontrolled real-world scenarios. Such degradations typically manifest as large-scale, multicolored moiré patterns. Moreover, these patterns frequently occur in images for which clean counterparts are difficult to obtain, such as photographs acquired from public displays or existing online resources. In this work, we propose a novel data engine designed to improve the removal of
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Improving Complex Moiré Removal with Generative Supervision” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 84%roboflow/supervision →
“Fuzzy title match (0.92): “Improving Complex Moiré Removal with Generative Supervision” ≈ “roboflow/supervision””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Improving Complex Moiré Removal with Generative Supervision” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%Xinyang Gu →
“Improving Complex Moiré Removal with Generative Supervision”
- LinkedLinked via arxiv author · 85%Zhilu Zhang →
“Improving Complex Moiré Removal with Generative Supervision”
- LinkedLinked via arxiv author · 85%Honglei Xu →
“Improving Complex Moiré Removal with Generative Supervision”
- LinkedLinked via arxiv author · 85%Yanting Mei →
“Improving Complex Moiré Removal with Generative Supervision”
- LinkedLinked via arxiv author · 85%Yukang Ding →
“Improving Complex Moiré Removal with Generative Supervision”