The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Mop Moiré Patterns Using MopNet
Abstract
Moiré pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moiré pattern make its removal a challenging problem. In this paper, we propose a Moiré pattern Removal Neural Network (MopNet) to solve this problem. All core components of MopNet are specially designed for unique properties of moire patterns, including the multi-scale feature aggregation addressing complex frequency, the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels, and the attribute-aware classifier to characterize the diverse appearance for better modelling Moiré patterns. Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet.
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Moiré patterns severely degrade the visual quality of captured photos and videos.
“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 [1]
Moiré patterns in camera-based screen capture are mainly caused by frequency aliasing between the camera's color filter array (CFA) and the display's sub-pixel structure.
“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 [1]
Recent multi-scale and U-Net-based demoiréing methods still fail to fully capture the global characteristics of moiré artifacts.
“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 [1]
Most existing demoiréing methods struggle to remove large-scale, globally distributed moiré patterns in both images and videos.
“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 [1]
The spatially varying intensity of moiré patterns within a frame occurs due to factors such as camera perspective distortion, lens aberrations, and nonlinear distortions introduced by the ISP pipeline.
“This phenomenon occurs due to factors such as the camera's perspective distortion, lens aberrations, and nonlinear distortions introduced by the ISP pipeline during the screen-capturing process [22, 1, 23].”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [1]
Uniform processing by existing demoiréing models often over-smooths the underlying content and compromises image fidelity.
“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 [1]
Image demoiréing models generally lack temporal modeling capabilities.
“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 [1]
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · cited 3×
“We further demonstrate whether the moiré artifacts can be effectively handled by other image restoration methods such as DnCNN [27], VDSD [6], and U-Net [19].”
MethodLearning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · cited 2×
“Also, general degradation oriented universal learning framework has been investigated, with prior of degradation kernel [28] or exploitation of self-similarity through non-local operation [14].”
MethodMoiré photo restoration using multiresolution convolutional neural networks
Sun, Y., Yu, Y., & Wang, W. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2834737 · Cited by 162
Flagged first, then by citations
Research works printed but not linked: 20
- 10.1109/CVPR.2018.00338 · DOI
- 10.1109/TPAMI.2007.1106 · DOI
- 10.1109/ICCV.2013.302 · DOI
- liu, 2018, Demoiréing of camera-captured screen images using deep convolutional neural network, arXiv preprint arXiv 1804 05809
- liu, 2018, Non-local recurrent network for image restoration, Advances in neural information processing systems, 1680
- liu, 2015, Moiré pattern removal from texture images via low-rank and sparse matrix decomposition, IEEE Visual Communications and Image Processing, 1
- 10.1109/CVPR.2017.35 · DOI
- nishioka, 2000, Endoscope system provided with low-pass filter for moire removal
- plummer, 1991, Anti-aliasing optical system with pyramidal transparent structure
- hu, 2018, Squeeze-and-excitation networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132
- 10.1109/TIP.2017.2662206 · DOI · Paper page
- 10.1109/CVPR.2017.243 · DOI · Paper page
- kim, 2018, Deep context-aware descreening and rescreening of halftone images, ACM Transactions on Graphics, 37, 48, 10.1145/3197517.3201377 · DOI
- 10.1109/CVPR.2012.6247952 · DOI · Paper page
- bayer, 1976, Color imaging array
- 10.1109/TIP.2017.2698920 · DOI
- yang, 2018, Towards perceptual image dehazing by physics-based disentanglement and adversarial training, AAAI Conference on Artificial Intelligence, 10.1609/aaai.v32i1.12317 · DOI
- yang, 2017, Demoiréing for screen-shot images with multi-channel layer decomposition, IEEE Visual Communications and Image Processing, 1
- zhang, 2017, Image de-raining using a conditional generative adversarial network, arXiv preprint arXiv 1701 08241
- 10.1109/CVPR.2018.00079 · DOI · Paper page
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
Std-net: spatio-temporal decomposition network for video demoiréing with sparse transformers
Niu, Y., Xu, R., Lin, Z., & Liu, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3386604 · Cited by 12
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
- He, B., Wang, C., Shi, B., & Duan, L.
- Affiliations
- Peking University (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 2424-2432
- OpenAlex
- W2984466630
- Cited by
- 97
- References
- 8
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 20 · 2023: 10 · 2022: 10 · 2021: 6 · 2020: 12
- FWCI
- 2.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (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 Video Quality Assessment
- Keywords
- aliasing, multi-scale feature aggregation, image degradation
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