参考文献

参考文献#

[CCCY18]

Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. Image blind denoising with generative adversarial network based noise modeling. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, volume, 3155–3164. 2018. doi:10.1109/CVPR.2018.00333.

[CLL+15]

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274, 2015.

[CMJ+18]

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, and others. Tvm: an automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), 578–594. 2018.

[CSH05]

K Cullen, G Silvestre, and Neil Hurley. Simulation tools for fixed point dsp algorithms and architectures. International Journal of Signal Processing, 1:199–203, 01 2005. URL: https://www.researchgate.net/publication/235008367_Simulation_Tools_for_Fixed_Point_DSP_Algorithms_and_Architectures.

[GYZ+19]

Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang. Toward convolutional blind denoising of real photographs. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), volume, 1712–1722. 2019. doi:10.1109/CVPR.2019.00181.

[IS15]

Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015. URL: https://arxiv.org/abs/1502.03167, doi:10.48550/ARXIV.1502.03167.

[JMFU17]

Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey, and Michael Unser. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 26(9):4509–4522, sep 2017. URL: https://doi.org/10.1109%2Ftip.2017.2713099, doi:10.1109/tip.2017.2713099.

[Lef16]

Stamatios Lefkimmiatis. Non-local color image denoising with convolutional neural networks. 2016. URL: https://arxiv.org/abs/1611.06757, doi:10.48550/ARXIV.1611.06757.

[LLL+21]

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, and Depei Qian. The deep learning compiler: a comprehensive survey. IEEE Transactions on Parallel and Distributed Systems, 32(3):708–727, mar 2021. URL: https://doi.org/10.1109%2Ftpds.2020.3030548, doi:10.1109/tpds.2020.3030548.

[MLY+20]

Mingqiang Meng, Sui Li, Lisha Yao, Danyang Li, Manman Zhu, Qi Gao, Qi Xie, Qian Zhao, Zhaoying Bian, Jing Huang, Deyu Meng, Dong Zeng, and Jianhua Ma Sr. Semi-supervised learned sinogram restoration network for low-dose CT image reconstruction. In Guang-Hong Chen and Hilde Bosmans, editors, Medical Imaging 2020: Physics of Medical Imaging, volume 11312, 113120B. International Society for Optics and Photonics, SPIE, 2020. URL: https://doi.org/10.1117/12.2548985, doi:10.1117/12.2548985.

[RLK+19]

Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Josh Pollock, Logan Weber, Ziheng Jiang, Tianqi Chen, Thierry Moreau, and Zachary Tatlock. Relay: a high-level ir for deep learning. arXiv preprint arXiv:1904.08368, 2019.

[TFZ+19]

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. Deep learning on image denoising: an overview. 2019. URL: https://arxiv.org/abs/1912.13171, doi:10.48550/ARXIV.1912.13171.

[YLZ+18]

Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, and Liang Lin. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), volume, 814–81409. 2018. doi:10.1109/CVPRW.2018.00113.

[ZZC+17]

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7):3142–3155, jul 2017. URL: https://doi.org/10.1109%2Ftip.2017.2662206, doi:10.1109/tip.2017.2662206.

[ZZZ18]

Kai Zhang, Wangmeng Zuo, and Lei Zhang. FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Image Processing, 27(9):4608–4622, sep 2018. URL: https://doi.org/10.1109%2Ftip.2018.2839891, doi:10.1109/tip.2018.2839891.

[ZZZ19]

Kai Zhang, Wangmeng Zuo, and Lei Zhang. Deep plug-and-play super-resolution for arbitrary blur kernels. 2019. URL: https://arxiv.org/abs/1903.12529, doi:10.48550/ARXIV.1903.12529.