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Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13678))

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Abstract

With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i.e., ultra-high-definition) images. However, for image demoiréing, a challenging task in low-level vision, existing works are generally carried out on low-resolution or synthetic images. Hence, the effectiveness of these methods on 4K resolution images is still unknown. In this paper, we explore moiré pattern removal for ultra-high-definition images. To this end, we propose the first ultra-high-definition demoiréing dataset (UHDM), which contains 5,000 real-world 4K resolution image pairs, and conduct a benchmark study on current state-of-the-art methods. Further, we present an efficient baseline model ESDNet for tackling 4K moiré images, wherein we build a semantic-aligned scale-aware module to address the scale variation of moiré patterns. Extensive experiments manifest the effectiveness of our approach, which outperforms state-of-the-art methods by a large margin while being much more lightweight. Code and dataset are available at https://xinyu-andy.github.io/uhdm-page.

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References

  1. Anwar, S., Barnes, N.: Densely residual laplacian super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 44, 1192–1204 (2020)

    Google Scholar 

  2. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International conference on machine learning. PMLR, pp. 214–223 (2017)

    Google Scholar 

  3. Cao, D., Chen, Z., Gao, L.: An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks. Human-centric Computing and Information Sciences 10(1), 1–22 (2020). https://doi.org/10.1186/s13673-020-00219-9

    Article  Google Scholar 

  4. Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3291–3300 (2018)

    Google Scholar 

  5. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  6. Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE international conference on computer vision, pp. 1511–1520 (2017)

    Google Scholar 

  7. Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7103–7112 (2018)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3848–3856 (2019)

    Google Scholar 

  10. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, vol. 27 (2014)

    Google Scholar 

  11. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)

  12. 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)

    Google Scholar 

  13. He, B., Wang, C., Shi, B., Duan, L.-Y.: FHDe2Net: full high definition demoireing network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 713–729. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_43

    Chapter  Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708 (2017)

    Google Scholar 

  16. 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

    Chapter  Google Scholar 

  17. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1646–1654 (2016)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  19. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 136–144 (2017)

    Google Scholar 

  20. Liu, B., Shu, X., Wu, X.: Demoir\(\backslash \)’eing of camera-captured screen images using deep convolutional neural network. arXiv preprint arXiv:1804.03809 (2018)

  21. Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 85–100 (2018)

    Google Scholar 

  22. 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

    Chapter  Google Scholar 

  23. Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

  24. Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4151–4160 (2017)

    Google Scholar 

  25. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  26. Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1874–1883 (2016)

    Google Scholar 

  27. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  28. Song, Y., et al.: Contextual-based image inpainting: Infer, match, and translate. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

    Google Scholar 

  29. Sun, Y., Yu, Y., Wang, W.: Moiré photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160–4172 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  30. Suvorov, R., et al.: Resolution-robust large mask inpainting with fourier convolutions. arXiv preprint arXiv:2109.07161 (2021)

  31. Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8174–8182 (2018)

    Google Scholar 

  32. Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1469–1472 (2010)

    Google Scholar 

  33. Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349–3364 (2020)

    Article  Google Scholar 

  34. Wang, Yi., Chen, Ying-Cong., Tao, Xin, Jia, Jiaya: VCNet: a robust approach to blind image inpainting. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 752–768. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_45

    Chapter  Google Scholar 

  35. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  36. Xie, C., et al.: Image inpainting with learnable bidirectional attention maps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8858–8867 (2019)

    Google Scholar 

  37. 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)

    Google Scholar 

  38. Yeh, R., Chen, C., Lim, T.Y., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with perceptual and contextual losses. arXiv preprint arXiv:1607.07539 2(3) (2016)

  39. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  40. Yuan, S., et al.: Aim 2019 challenge on image demoireing: Methods and results. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3534–3545. IEEE (2019)

    Google Scholar 

  41. Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14821–14831 (2021)

    Google Scholar 

  42. Zhang, H., Dai, Y., Li, H., Koniusz, P.: Deep stacked hierarchical multi-patch network for image deblurring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5978–5986 (2019)

    Google Scholar 

  43. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  44. 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)

    Google Scholar 

  45. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472–2481 (2018)

    Google Scholar 

  46. 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)

    Google Scholar 

  47. Zhou, T., Tucker, R., Flynn, J., Fyffe, G., Snavely, N.: Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817 (2018)

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Acknowledgements

This work is partially supported by HKU-TCL Joint Research Center for Artificial Intelligence, Hong Kong Research Grant Council - Early Career Scheme (Grant No. 27209621), National Key R &D Program of China (No.2021YFA1001300), and Guangdong-Hong Kong-Macau Applied Math Center grant 2020B1515310011.

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Authors and Affiliations

  1. The University of Hong Kong, Hong Kong, China

    Xin Yu, Peng Dai & Xiaojuan Qi

  2. The Chinese University of Hong Kong, Hong Kong, China

    Wenbo Li

  3. TCL AI Lab, Hong Kong, China

    Lan Ma & Jiajun Shen

  4. Sun Yat-sen University, Guangzhou, China

    Jia Li

Authors
  1. Xin Yu
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  2. Peng Dai
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  3. Wenbo Li
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  4. Lan Ma
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  5. Jiajun Shen
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  6. Jia Li
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  7. Xiaojuan Qi
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Corresponding author

Correspondence to Xiaojuan Qi .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Yu, X. et al. (2022). Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_37

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  • DOI: https://doi.org/10.1007/978-3-031-19797-0_37

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