The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Moiré Pattern Removal via Attentive Fractal Network
Abstract
Moiré patterns are commonly seen artifacts when taking photos of screens and other objects with high-frequency textures. It's challenging to remove the moiré patterns considering its complex color and shape. In this work, we propose an Attentive Fractal Network to effectively solve this problem. First, we construct each Attentive Fractal Block with progressive feature fusion and channel-wise attention guidance. The network is then fractally stacked with the block on each of its levels. Second, to further boost the performance, we adopt a two-stage augmented refinement strategy. With these designs, our method wins the burst demoiréing track and achieves second place in single image demoireing and single image deblurring tracks inNTIRE20 Challenges. Extensive experiments demonstrate the superiority of our method for moiré pattern removal compared to existing state-of-the-art methods, and prove the effectiveness of its each component. We will publicly release our code and trained weights on https://github.com/ir1d/AFN.
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In the citing paper
“Second, most existing demoir´ eing methods [7, 1, 13, 5, 16, 17, 18] struggle to remove large-scale, globally distributed moir´ e patterns in both images and videos.”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [13]
In the citing paper
“As a result, not only are moir´ e patterns removed, but also the underlying content is often over-smoothed, compromising image fidelity [13, 1].”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [13]
In the citing paper
“Fourth, image demoir´ eing models [7, 1, 13, 5] generally lack temporal modeling capabilities.”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [13]
Citations
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He, B., Wang, C., Shi, B., & Duan, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00251 · Cited by 97
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Cited by
Sorted by citations
Recaptured screen image demoiréing in raw domain
Yue, H., Cheng, Y., Mao, Y., Cao, C., & Yang, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3198333 · Cited by 26
MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing
Sung, J., Roh, C., Eom, C., & Oh, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2025.132477
Metadata
- Authors
- Xu, D., Chu, Y., & Sun, Q.
- Affiliations
- Peking University (CN)
- Beijing University of Posts and Telecommunications (CN)
- Beijing Jiaotong University (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1943-1952
- OpenAlex
- W3034935378
- Cited by
- 19
- References
- 6
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 2
- FWCI
- 0.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- fractal networks, channel attention, progressive feature fusion, single image deblurring, image artifact removal
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