Cited by 62Advanced 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.

FHDe2Net: Full High Definition Demoireing Network

He, Bin, Wang, Ce, Shi, Boxin, Duan, Ling-Yu·2020·Lecture Notes in Computer Science

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-030-58542-6_43

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Show all 2 references

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

Cited by

Sorted by citations

Metadata

Authors
He, B., Wang, C., Shi, B., & Duan, L.-Y.
Affiliations
Peking University (CN)
Corresponding author
Duan, Ling-Yu
Venue
Lecture Notes in Computer Science
Published
2020
Pages
713-729
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review