1. Amos, B., Kolter, J.Z.: Optnet: Differentiable optimization as a layer in neural networks. In: International Conference on Machine Learning, pp. 136–145 (2017)

   Google Scholar 

2. Chen, F., Cheung, G., Zhang, X.: Fast & robust image interpolation using gradient graph laplacian regularizer. In: IEEE International Conference on Image Processing, pp. 1964–1968 (2021)

   Google Scholar 

3. Chen, F., Cheung, G., Zhang, X.: Manifold graph signal restoration using gradient graph laplacian regularizer. IEEE Transactions on Signal Processing (2024)

   Google Scholar 

4. Chen, F., Cheung, G., Zhang, X.: Soft image segmentation using gradient graph laplacian regularizer. IEEE International Conference on Acoustics, Speech and Signal Processing (2024)

   Google Scholar 

5. Chen, H., Wei, Z., Li, X., Xu, Y., Wei, M., Wang, J.: Repcd-net: Feature-aware recurrent point cloud denoising network. Int. J. Comput. Vision130(3), 615–629 (2022)

   Article  Google Scholar 

6. Chen, Z., Li, P., Wei, Z., Chen, H., Xie, H., Wei, M., Wang, F.L.: Geogcn: Geometric dual-domain graph convolution network for point cloud denoising. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1–5 (2023)

   Google Scholar 

7. Dinesh, C., Cheung, G., Bajić, I.V.: 3d point cloud color denoising using convex graph-signal smoothness priors. In: 21st International Workshop on Multimedia Signal Processing, pp. 1–6 (2019)

   Google Scholar 

8. d’Eon, E., Harrison, B., Myers, T., Chou, P.A.: 8i voxelized full bodies-a voxelized point cloud dataset. ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M740067(8), 11 (2017)

   Google Scholar 

9. Gharedaghi, Y., Cheung, G., Liu, X.: Retinex-based image denoising / contrast enhancement using gradient graph laplacian regularizer. IEEE International Conference on Image Processing pp. 2710–2714 (2023)

   Google Scholar 

10. Hu, W., Cheung, G., Kazui, M.: Graph-based dequantization of block-compressed piecewise smooth images. IEEE Signal Process. Lett.23(2), 242–246 (2015)

    Article  Google Scholar 

11. Huang, Y., Li, S., Wang, L., Tan, T., et al.: Unfolding the alternating optimization for blind super resolution. Adv. Neural. Inf. Process. Syst.33, 5632–5643 (2020)

    Google Scholar 

12. Irfan, M.A., Magli, E.: Point cloud denoising using joint geometry/color graph wavelets. In: Workshop on Signal Processing Systems, pp. 1–6 (2020)

    Google Scholar 

13. Irfan, M.A., Magli, E.: Exploiting color for graph-based 3d point cloud denoising. J. Vis. Commun. Image Represent.75, 103027 (2021)

    Article  Google Scholar 

14. Li, J., Li, M., Li, Z., Peng, S.: Super-voxel graph guided 3d point cloud denoising. In: International Conference on Computer Research and Development, pp. 276–280 (2022)

    Google Scholar 

15. Lin, Y., Chen, F., Cheng, H., Wang, M.: Handwriting curve interpolation using gradient graph laplacian regularizer. IEEE International Conference on Multimedia and Expo pp. 1649–1654 (2023)

    Google Scholar 

16. Liu, X., Cheung, G., Wu, X., Zhao, D.: Random walk graph laplacian-based smoothness prior for soft decoding of jpeg images. IEEE Trans. Image Process.26(2), 509–524 (2016)

    Article MathSciNet  Google Scholar 

17. Liu, X., Zhai, D., Zhao, D., Zhai, G., Gao, W.: Progressive image denoising through hybrid graph laplacian regularization: A unified framework. IEEE Trans. Image Process.23(4), 1491–1503 (2014)

    Article MathSciNet  Google Scholar 

18. Luo, S., Hu, W.: Score-based point cloud denoising. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4583–4592 (2021)

    Google Scholar 

19. Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: A simple residual mlp framework. In: International Conference on Learning Representations (2022)

    Google Scholar 

20. Mou, C., Wang, Q., Zhang, J.: Deep generalized unfolding networks for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17399–17410 (2022)

    Google Scholar 

21. Nakayama, K., Fukuta, H., Watanabe, H.: Point cloud denoising and outlier detection with local geometric structure by dynamic graph cnn. In: IEEE 12th Global Conference on Consumer Electronics, pp. 156–159 (2023)

    Google Scholar 

22. Nouri, A., Charrier, C., Lézoray, O.: Technical report: Greyc 3D colored mesh database. Ph.D. thesis, Normandie Université, Unicaen, EnsiCaen, CNRS, GREYC UMR 6072 (2017)

    Google Scholar 

23. Pang, J., Cheung, G.: Graph laplacian regularization for image denoising: Analysis in the continuous domain. IEEE Trans. Image Process.26(4), 1770–1785 (2017)

    Article MathSciNet  Google Scholar 

24. Peterson, L.E.: K-nearest neighbor. Scholarpedia4(2), 1883 (2009)

    Article  Google Scholar 

25. Pistilli, F., Fracastoro, G., Valsesia, D., Magli, E.: Learning robust graph-convolutional representations for point cloud denoising. IEEE Journal of Selected Topics in Signal Processing15(2), 402–414 (2020)

    Article  Google Scholar 

26. Rakotosaona, M.J., La Barbera, V., Guerrero, P., Mitra, N.J., Ovsjanikov, M.: Pointcleannet: Learning to denoise and remove outliers from dense point clouds. In: Computer graphics forum, vol. 39, pp. 185–203 (2020)

    Google Scholar 

27. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention, pp. 234–241. Springer (2015)

    Google Scholar 

28. Scetbon, M., Elad, M., Milanfar, P.: Deep k-svd denoising. IEEE Trans. Image Process.30, 5944–5955 (2021)

    Article MathSciNet  Google Scholar 

29. Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag.30(3), 83–98 (2013)

    Article  Google Scholar 

30. Tang, W., Gong, Y., Qiu, G.: Feature preserving 3d mesh denoising with a dense local graph neural network. Comput. Vis. Image Underst.233, 103710 (2023)

    Article  Google Scholar 

31. Vaksman, G., Elad, M., Milanfar, P.: Lidia: Lightweight learned image denoising with instance adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 524–525 (2020)

    Google Scholar 

32. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Trans. Graph.38(5), 1–12 (2019)

    Article  Google Scholar 

33. Watanabe, R., Nonaka, K., Kato, H., Pavez, E., Kobayashi, T., Ortega, A.: Point cloud denoising using normal vector-based graph wavelet shrinkage. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2569–2573 (2022)

    Google Scholar 

34. Watanabe, R., Nonaka, K., Pavez, E., Kobayashi, T., Ortega, A.: Graph-based point cloud color denoising with 3-dimensional patch-based similarity. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1–5 (2023)

    Google Scholar 

35. Wei, M., Chen, H., Zhang, Y., Xie, H., Guo, Y., Wang, J.: Geodualcnn: Geometry-supporting dual convolutional neural network for noisy point clouds. IEEE Trans. Visual Comput. Graphics29(2), 1357–1370 (2021)

    Article  Google Scholar 

36. Xu, Z., Foi, A.: Anisotropic denoising of 3d point clouds by aggregation of multiple surface-adaptive estimates. IEEE Trans. Visual Comput. Graphics27(6), 2851–2868 (2019)

    Article  Google Scholar 

37. Yang, D., Sun, J.: Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In: Proceedings of the european conference on computer vision, pp. 702–717 (2018)

    Google Scholar 

38. Zeng, J., Pang, J., Sun, W., Cheung, G.: Deep graph laplacian regularization for robust denoising of real images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0 (2019)

    Google Scholar 

39. Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3217–3226 (2020)

    Google Scholar 

Download references