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
Cites
Squeeze-and-Excitation Networks
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Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
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Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_18 · Cited by 6,003
Accelerating the Super-Resolution Convolutional Neural Network
Dong, C., Loy, C. C., & Tang, X. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_25 · Cited by 3,786
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757
GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond
Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00246 · Cited by 2,307
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P. L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2785879 · Cited by 1,095
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Sajjadi, M. S. M., Schölkopf, B., & Hirsch, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.481 · Cited by 1,088
Research works printed but not linked: 2
- 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)
Cited by
Learning Frequency Domain Priors for Image Demoireing
Zheng, B., Yuan, S., Yan, C. C., Tian, X., Zhang, J., Sun, Y., Liu, L., Leonardis, A., & Slabaugh, G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3115139 · Cited by 91
Self-citationSingle Image Depth Prediction with Wavelet Decomposition
Ramamonjisoa, M., Firman, M., Watson, J., Lepetit, V., & Turmukhambetov, D. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01094 · Cited by 89
Recurrent wavelet structure-preserving residual network for single image deraining
Hsu, W., & Chang, W.-C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109294 · Cited by 45
Spatio-Temporal Detail Information Retrieval for Compressed Video Quality Enhancement
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-citation
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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