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Cited by 14Advanced Image Processing TechniquesImage Enhancement TechniquesImage and Video Quality Assessment

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Image Demoiréing in RAW and sRGB Domains

Xu, Shuning, Song, Binbin, Chen, Xiangyu, Liu, Xina, Zhou, Jiantao·2025·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-72658-3_7
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Crossref · 10.1007/978-3-031-72658-3_7

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Moiré patterns exhibit different characteristics in the sRGB and RAW domains.

    “Moir´ e patterns exhibit different characteristics in the sRGB and RAW domains [17, 2, 5].”

    From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [2]

  • 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 [2]

  • 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 [2]

  • 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 [2]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 30

  1. Cheng, X., Fu, Z., Yang, J.: Multi-scale dynamic feature encoding network for image demoiréing. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3486–3493. IEEE (2019) · DOI · Paper page
  2. Dai, P., et al.: Video demoireing with relation-based temporal consistency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17622–17631 (2022) · DOI · Paper page
  3. Dong, X., et al.: Abandoning the bayer-filter to see in the dark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17431–17440 (2022) · DOI
  4. Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. ACM Trans. Graph. (ToG) 35(6), 1–12 (2016) · DOI
  5. He, B., Wang, C., Shi, B., Duan, L.Y.: Mop moire patterns using mopnet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2424–2432 (2019) · DOI · Paper page
  6. Denninger, M., Triebel, R.: 3D scene reconstruction from a single viewport. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 51–67. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_4 · DOI
  7. Huang, H., Yang, W., Hu, Y., Liu, J., Duan, L.Y.: Towards low light enhancement with raw images. IEEE Trans. Image Process. 31, 1391–1405 (2022) · DOI
  8. Jin, X., Han, L.H., Li, Z., Guo, C.L., Chai, Z., Li, C.: Dnf: decouple and feedback network for seeing in the dark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18135–18144 (2023) · DOI
  9. Khashabi, D., Nowozin, S., Jancsary, J., Fitzgibbon, A.W.: Joint demosaicing and denoising via learned nonparametric random fields. IEEE Trans. Image Process. 23(12), 4968–4981 (2014) · DOI
  10. Kokkinos, F., Lefkimmiatis, S.: Deep image demosaicking using a cascade of convolutional residual denoising networks. In: Proceedings of the European conference on computer vision (ECCV), pp. 303–319 (2018) · DOI
  11. Lei, C., Chen, Q.: Robust reflection removal with reflection-free flash-only cues. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14811–14820 (2021) · DOI
  12. Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 1833–1844 (2021) · DOI
  13. Liu, L., Jia, X., Liu, J., Tian, Q.: Joint demosaicing and denoising with self guidance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2240–2249 (2020) · DOI
  14. Liu, L., et al.: Wavelet-based dual-branch network for image demoiréing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 86–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_6 · DOI
  15. Liu, S., Li, C., Nan, N., Zong, Z., Song, R.: Mmdm: multi-frame and multi-scale for image demoiréing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 434–435 (2020) · DOI
  16. Luo, F., Wu, X., Guo, Y.: And: adversarial neural degradation for learning blind image super-resolution. Adv. Neural Inform. Process. Syst. 36 (2024)
  17. Nakamura, J.: Image sensors and signal processing for digital still cameras. CRC press (2017) · DOI
  18. Niu, Y., Lin, Z., Liu, W., Guo, W.: Progressive moire removal and texture complementation for image demoireing. IEEE Trans. Circ. Syst. Video Technol. (2023) · DOI
  19. Oh, G., Gu, H., Kim, S., Kim, J.: Fpanet: Frequency-based video demoireing using frame-level post alignment. arXiv preprint arXiv:2301.07330 (2023) · arXiv
  20. Song, B., Zhou, J., Chen, X., Zhang, S.: Real-scene reflection removal with raw-rgb image pairs. IEEE Trans. Circ. Syst. Video Technol. (2023) · DOI
  21. Wang, C., He, B., Wu, S., Wan, R., Shi, B., Duan, L.Y.: Coarse-to-fine disentangling demoiréing framework for recaptured screen images. IEEE Trans. Pattern Anal. Mach. Intell. (2023) · DOI · Paper page
  22. Wang, H., Tian, Q., Li, L., Guo, X.: Image demoiréing with a dual-domain distilling network. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2021) · DOI
  23. Xing, W., Egiazarian, K.: End-to-end learning for joint image demosaicing, denoising and super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3507–3516 (2021) · DOI
  24. Xu, S., Song, B., Chen, X., Zhou, J.: Direction-aware video demoireing with temporal-guided bilateral learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 6360–6368 (2024) · DOI · Paper page
  25. Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., Qi, X.: Towards efficient and scale-robust ultra-high-definition image demoiréing. In: European Conference on Computer Vision. pp. 646–662. Springer (2022). https://doi.org/10.1007/978-3-031-19797-0_37 · DOI · Paper page
  26. Yue, H., Cheng, Y., Liu, X., Yang, J.: Recaptured raw screen image and video demoireing via channel and spatial modulations. arXiv preprint arXiv:2310.20332 (2023) · DOI · Paper page
  27. Yue, H., Cheng, Y., Mao, Y., Cao, C., Yang, J.: Recaptured screen image demoiréing in raw domain. IEEE Trans. Multimedia (2022) · DOI · Paper page
  28. Yue, H., Zhang, Z., Yang, J.: Real-rawvsr: real-world raw video super-resolution with a benchmark dataset. In: European Conference on Computer Vision. pp. 608–624. Springer (2022). https://doi.org/10.1007/978-3-031-20068-7_35 · DOI
  29. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018) · DOI · Paper page
  30. Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3636–3645 (2020) · DOI

Cited by

Sorted by citations

Metadata

Authors
Xu, S., Song, B., Chen, X., Liu, X., & Zhou, J.
Affiliations
  • University of Macau (MO)
  • Chinese Academy of Sciences (CN)
Corresponding author
Zhou, Jiantao
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2024-10-02
Pages
108-124
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-72658-3_7
OpenAlex
W4403081415
Cited by
14
References
2
Citations by year
2026: 6 · 2025: 8
FWCI
2.90
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (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
raw images, sRGB, color cast correction, image signal processor, skip connections, screen capture, smartphone imaging

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