The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Deep wavelet network with domain adaptation for single image demoiréing
Abstract
Convolutional neural networks have made a prominent progress in low-level image restoration tasks. Moire is a kind of high-frequency and irregular interference stripe that appears on the photosensitive element of digital cameras or scanners. It can bring in unpleasant colorful artifacts on images. In this paper, we propose a deep wavelet network with domain adaptation mechanism for single image demoireing, dubbed AWUDN. The feature mapping is mainly performed in the wavelet domain, which can not only cut down computation complexity, but also reduce information loss. Moreover, considering that the images provided by the challenge organizers have strong self-similarity, the global context block is adopted for the learning of feature dependency in different positions. Finally, we introduce the domain adaptation mechanism to fine-tune the pretrained model for reducing the domain gap between training moire dataset and testing moire dataset. Benefiting from these improvements, the proposed method can achieve superior accuracy on the public testing dataset in the NTIRE 2020 Single Image Demoireing Challenge.
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In the citing paper
“frequency aliasing between the camera's color filter array (CFA) and the display's sub-pixels induces moir´ e patterns that severely degrade captured photos and videos.”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [6]
In the citing paper
“While recent methods adopt multi-scale architectures [7, 1, 8, 14] and symmetric encoder-decoder networks such as U-Net [24, 6, 2], they still fail to fully capture the global characteristics of moir´ e artifacts.”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [6]
In the citing paper
“These moir´ e artifacts are mainly caused by frequency aliasing between the camera's color filter array (CFA) and the sub-pixel structure of the screen [1, 2, 3, 4, 5, 6].”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [6]
Citations
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- gao, 2019, Moiré pattern removal with multi-scale feature enhancing network, In ICME
- liu, 2015, Moiré pattern removal from texture images via low-rank and sparse matrix decomposition, Visual Communications and Image Processing (VCIP)
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Luo, D., Ye, M., Li, S., Zhu, C., & Li, X. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3214775 · Cited by 40
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Yuan, S., Timofte, R., Leonardis, A., Slabaugh, G., Luo, X., Zhang, J., Qu, Y., Hong, M., Xie, Y., Li, C., et al. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00238 · Cited by 39
Self-citationWavelet Approximation-Aware Residual Network for Single Image Deraining
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Efficient Attention Fusion Network in Wavelet Domain for Demoireing
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Image Demoiréing via Multiscale Fusion Networks With Moiré Data Augmentation
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Pyramid Learnable Bandpass Filters for Ultra-High-Definition Image Demoiréing
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Single Image Depth Prediction with Wavelet DecompositionPreprint
Ramamonjisoa, M., Firman, M., Watson, J., Lepetit, V., & Turmukhambetov, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02022 · Cited by 3
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3D scene reconstruction from images
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D$^{2}$Net: Diagonal-Guided Dual-Stream Network Based on Wavelet Transform for Image Demoiréing
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MoiréNet: Leveraging Directional Priors for Compact Dual-Domain Image Demoiréing
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Dual-Camera Video Demoiréing for Smartphones
Li, X., Xiao, Z., Zou, Y., Li, X., Liu, L., ZHAO, Y., & Weijia Jia, B. (2026) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3767308.3835271
Metadata
- Authors
- Luo, X., Zhang, J., Hong, M., Qu, Y., Xie, Y., & Li, C.
- Affiliations
- Xiamen University (CN)
- East China Normal University (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1687-1694
- OpenAlex
- W3034651384
- Cited by
- 24
- References
- 17
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 1
- FWCI
- 1.23
- 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 and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- domain adaptation, pre-trained model fine-tuning
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