[1] Chen Chen, Qifeng Chen, Minh N Do, and Vladlen Koltun. Seeing motion in the dark. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3185-3194, 2019.
[2] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3291-3300, 2018.
[3] Peng Dai, Xin Yu, Lan Ma, Baoheng Zhang, Jia Li, Wenbo Li, Jiajun Shen, and Xiaojuan Qi. Video demoireing with relation-based temporal consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17622-17631, 2022.
[4] Hansen Feng, Lizhi Wang, Yuzhi Wang, Haoqiang Fan, and Hua Huang. Learnability enhancement for low-light raw image denoising: A data perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[5] Bin He, Ce Wang, Boxin Shi, and Ling-Yu Duan. Mop moiré patterns using mopnet. In Proceedings of the IEEE International Conference on Computer Vision, pages 2424-2432, 2019.
[6] Bin He, Ce Wang, Boxin Shi, and Ling-Yu Duan. FHD2Net full high definition demoiring network. In Proceedings of the European Conference on Computer Vision, pages 713-729. Springer, 2020.
[7] Qibin Hou, Cheng-Ze Lu, Ming-Ming Cheng, and Jiashi Feng. Conv2former: A simple transformer-style convnet for visual recognition. arXiv preprint arXiv:2211.11943, 2022.
[8] Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, and Luc Van Gool. Vrt: A video restoration transformer. arXiv preprint arXiv:2201.12288, 2022.
[9] Fanglei Liu, Jingyu Yang, and Huanjing Yue. Moiré pattern removal from texture images via low-rank and sparse matrix decomposition. In 2015 Visual Communications and Image Processing (VCIP), pages 1-4. IEEE, 2015.
[10] Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, et al. Learning raw image denoising with bayer pattern unification and bayer preserving augmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2070-2077, 2019.
[11] Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, and Qi Tian. Wavelet-based dual-branch network for image demoiring. In Proceedings of the European Conference on Computer Vision, pages 86–102. Springer, 2020.
[12] Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, and Qi Tian. Self-adaptively learning to demoiré from focused and defocused image pairs. Advances in Neural Information Processing Systems, 33:22282-22292, 2020.
[13] Xiaohong Liu, Kangdi Shi, Zhe Wang, and Jun Chen. Exploit camera raw data for video super-resolution via hidden markov model inference. IEEE Transactions on Image Processing, 30:2127-2140, 2021.
[14] Yuzhen Niu, Zhihua Lin, Wenxi Liu, and Wenzhong Guo. Progressive moire removal and texture complementation for image demoiring. IEEE Transactions on Circuits and Systems for Video Technology, 2023.
[15] Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 724-732, 2016.
[16] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015.
[17] H. Siddiqui, M. Boutin, and C. Bouman. Hardware-friendly descreening. IEEE Transactions on Image Processing (TIP), 19(3):746-757, 2010.
[18] Bin Sun, Shutao Li, and Jun Sun. Scanned image descreening with image redundancy and adaptive filtering. IEEE Transactions on Image Processing (TIP), 23(8):3698-3710, 2014.
[19] Yujing Sun, Yizhou Yu, and Wenping Wang. Moiré photo restoration using multiresolution convolutional neural networks. IEEE Transactions on Image Processing, 27(8):4160-4172, 2018.
[20] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019.
[21] Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. Proceedings of the European Conference on Computer Vision, pages 1-16, 2020.
[22] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17683-17693, 2022.
[23] Kaixuan Wei, Ying Fu, Jiaolong Yang, and Hua Huang. A physics-based noise formation model for extreme low-light raw denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2758-2767, 2020.
[24] Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid. Deepflow: Large displacement optical flow with deep matching. In Proceedings of the IEEE International Conference on Computer Vision, pages 1385-1392, 2013.