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Cited by 62Advanced Image Processing TechniquesImage Enhancement TechniquesImage and Video Quality Assessment

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FHDe2Net: Full High Definition Demoireing Network

He, Bin, Wang, Ce, Shi, Boxin, Duan, Ling-Yu·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-58542-6_43
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Research works printed but not linked: 36

  1. Cheng, X., Fu, Z., Yang, J.: Multi-scale dynamic feature encoding network for image demoiréing. arXiv preprint arXiv:1909.11947 (2019) · DOI · Paper page
  2. Guo, J., Chao, H.: Building dual-domain representations for compression artifacts reduction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_38 · DOI
  3. Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1712–1722 (2019) · DOI · Paper page
  4. He, B., Wang, C., Shi, B., Duan, L.Y.: Mop moire patterns using mopnet. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2424–2432 (2019) · DOI · Paper page
  5. Huang, H., Nie, G., Zheng, Y., Fu, Y.: Image restoration from patch-based compressed sensing measurement. Neurocomputing 340, 145–157 (2019) · DOI
  6. Ignatov, A., et al.: Aim 2019 challenge on raw to RGB mapping: methods and results. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 5, p. 7 (2019) · DOI
  7. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43 · DOI · Paper page
  8. Kim, T.H., Park, S.I.: Deep context-aware descreening and rescreening of halftone images. ACM Trans. Graph. 37(4), 1–12 (2018)
  9. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN: blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8183–8192 (2018) · DOI
  10. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017) · DOI · Paper page
  11. Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3204–3213 (2018) · DOI
  12. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: AOD-Net: all-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017) · DOI
  13. Li, S., et al.: Single image deraining: a comprehensive benchmark analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3838–3847 (2019) · DOI
  14. Li, X., Wu, J., Lin, Z., Liu, H., Zha, H.: Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Proceedings of the European Conference on Computer Vision, pp. 254–269 (2018) · DOI
  15. Liu, B., Shu, X., Wu, X.: Demoiréing of camera-captured screen images using deep convolutional neural network. arXiv preprint, arXiv:1804.03809 (2018) · arXiv
  16. Liu, D., Wen, B., Fan, Y., Loy, C.C., Huang, T.S.: Non-local recurrent network for image restoration. In: Advances in Neural Information Processing Systems, pp. 1680–1689 (2018)
  17. Liu, F., Yang, J., Yue, H.: Moiré pattern removal from texture images via low-rank and sparse matrix decomposition. In: Proceedings of the IEEE Visual Communications and Image Processing, pp. 1–4 (2015) · DOI · Paper page
  18. Menon, D., Calvagno, G.: Color image demosaicking: an overview. Sig. Process. Image Commun. 26(8–9), 518–533 (2011) · DOI
  19. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979) · DOI
  20. Pekkucuksen, I., Altunbasak, Y.: Multiscale gradients-based color filter array interpolation. IEEE Trans. Image Process. 22(1), 157–165 (2012) · DOI
  21. Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586–1595 (2017) · DOI
  22. Schöberl, M., Schnurrer, W., Oberdörster, A., Fößel, S., Kaup, A.: Dimensioning of optical birefringent anti-alias filters for digital cameras. In: IEEE International Conference on Image Processing, pp. 4305–4308 (2010) · DOI
  23. Shen, Z., Huang, M., Shi, J., Xue, X., Huang, T.S.: Towards instance-level image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3683–3692 (2019) · DOI
  24. Siddiqui, H., Boutin, M., Bouman, C.A.: Hardware-friendly descreening. IEEE Trans. Image Process. 19(3), 746–757 (2009) · DOI
  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) · arXiv
  26. Sun, B., Li, S., Sun, J.: Scanned image descreening with image redundancy and adaptive filtering. IEEE Trans. Image Process. 23(8), 3698–3710 (2014) · DOI
  27. Wan, R., Shi, B., Duan, L.Y., Tan, A.H., Kot, A.C.: Benchmarking single-image reflection removal algorithms. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3922–3930 (2017) · DOI
  28. Wan, R., Shi, B., Duan, L.Y., Tan, A.H., Kot, A.C.: CRRN: multi-scale guided concurrent reflection removal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4777–4785 (2018) · DOI
  29. Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6721–6729 (2017) · DOI
  30. Yang, J., Liu, F., Yue, H., Fu, X., Hou, C., Wu, F.: Textured image demoiréing via signal decomposition and guided filtering. IEEE Trans. Image Process. 26(7), 3528–3541 (2017) · DOI
  31. Yang, J., Zhang, X., Cai, C., Li, K.: Demoiréing for screen-shot images with multi-channel layer decomposition. In: Proceedings of the IEEE Visual Communications and Image Processing, pp. 1–4 (2017) · DOI
  32. Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7234–7243 (2019) · DOI
  33. Zhang, H., Patel, V.M.: Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 695–704 (2018) · DOI · Paper page
  34. 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
  35. Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3762–3770 (2019) · DOI
  36. Zhang, X., Ng, R., Chen, Q.: Single image reflection separation with perceptual losses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4786–4794 (2018) · DOI

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Metadata

Authors
He, B., Wang, C., Shi, B., & Duan, L.-Y.
Affiliations
Peking University (CN)
Corresponding author
Duan, Ling-Yu
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Pages
713-729
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-58542-6_43
OpenAlex
W3106415285
Cited by
62
References
2
Citations by year
2026: 15 · 2025: 14 · 2024: 13 · 2023: 10 · 2022: 8 · 2021: 2
FWCI
1.54
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
aliasing, fine detail preservation, cascaded networks

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CoChat. (2026). Citation check: FHDe2Net: Full High Definition Demoireing Network. https://app.cochat.ai/papers/10.1007/978-3-030-58542-6_43
The paper
He, B., Wang, C., Shi, B., & Duan, L.-Y. (2020). FHDe2Net: Full High Definition Demoireing Network. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-58542-6_43