mlsanityData to 7 Oct 2026

UniDemoir'e: Towards Universal Image Demoir'eing with Data Generation and Synthesis

arXiv 2502.06324 ↗First posted 10 Feb 2025cs.CV cs.AIcited by 6

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Abstract (arXiv)

Image demoir'eing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moir'e patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moir'e domain, resulting in performance degradation for new domains and restricting their robustness in real-world applications. In this paper, we propose a universal image demoir'eing solution, UniDemoir'e, which has superior generalization capability. Notably, we propose innovative and effective data generation and synthesis methods that can automatically provide vast high-quality moir'e images to train a universal demoir'eing model. Our extensive experiments demonstrate the cutting-edge performance and broad potential of our approach for generalized image demoir'eing.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

27 of 54 references.

References · 27

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15 more
  1. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  2. 2016Generating Images with Perceptual Similarity Metrics based on Deep Networkscited by 221
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Modelscited by 9
  2. 2025Moir'e Zero: An Efficient and High-Performance Neural Architecture for Moir'e Removalcited by 1
  3. 2025SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstructioncited by 0
  4. 2026Fabric Image Demoir'eing Benchmark from Synthesis to Restorationcited by 0
  5. 2026Improving Complex Moir'e Removal with Generative Supervisioncited by 0
  6. 2026DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answeringcited by 0