Deep Image Denoising With Adaptive Priors

IEEE Transactions on Circuits and Systems for Video Technology - Tập 32 Số 8 - Trang 5124-5136 - 2022
Bo Jiang1, Yao Lu1, Jiahuan Wang1, Guangming Lu1, David Zhang2
1Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
2School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

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Tài liệu tham khảo

10.1109/TCSVT.2019.2890880

10.1109/TCSVT.2019.2930305

10.1109/TCSVT.2019.2927603

10.1109/TCSVT.2020.3034649

10.1016/0167-2789(92)90242-F

10.1109/TIP.2012.2221729

10.1109/CVPR.2014.366

10.1007/978-3-030-01237-3_2

10.1109/TCSVT.2018.2878794

10.1109/TCSVT.2017.2759187

10.1109/CVPR.2018.00984

Enderich, 2020, Holistic filter pruning for efficient deep neural networks, arXiv:2009.08169

10.3390/info11020125

DeVries, 2017, Improved regularization of convolutional neural networks with cutout, arXiv:1708.04552

Lee, 2019, Mixout: Effective regularization to finetune large-scale pretrained language models, arXiv:1909.11299

10.1109/ICCV.2019.00612

Gong, 2020, MaxUp: A simple way to improve generalization of neural network training, arXiv:2002.09024

Gastaldi, 2017, Shake-shake regularization, arXiv:1705.07485

Yamada, 2018, ShakeDrop regularization, arXiv:1802.02375

10.1109/TCSVT.2017.2654543

10.1007/978-3-319-46493-0_38

10.1109/TPAMI.2016.2596743

10.1109/CVPR.2017.623

Liu, Non-local recurrent network for image restoration, Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 1

10.1109/TPAMI.2019.2921548

10.1109/TPAMI.2018.2873610

10.1109/TIP.2017.2662206

10.1109/TIP.2018.2839891

10.1109/ICCV.2019.00325

10.1109/ICCV.2011.6126278

10.1109/TIP.2018.2811546

10.1109/ICCV.2009.5459452

10.1109/CVPR.2005.38

10.1109/TIP.2007.901238

10.1109/ICCV.2017.125

10.1609/aaai.v34i07.7000

10.1007/978-3-319-24574-4_28

Ng, On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes, Proc. NIPS, 1

10.1109/TPAMI.2019.2913372

Higgins, $Beta$ -VAE: Learning basic visual concepts with a constrained variational framework, Proc. Int. Conf. Learn. Represent. (ICLR), 1

10.1109/CVPR.2012.6247952

10.1109/ICCVW.2017.130

10.1109/ICCV.2017.486

Yue, Variational denoising network: Toward blind noise modeling and removal, Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 1

10.1007/978-3-030-58577-8_11

10.1109/CVPR42600.2020.00354

10.1109/TCI.2016.2644865

10.1109/TIP.2003.819861

10.1109/CVPRW.2017.151

10.1109/ICCV.2001.937655

10.1109/CVPRW.2017.150

10.1109/CVPR.2018.00182

Kingma, 2014, Adam: A method for stochastic optimization, arXiv:1412.6980

10.1109/CVPR.2018.00068

10.1109/CVPR.2005.160

10.1109/CVPR.2017.294

10.1109/CVPR.2016.186

10.1109/CVPR.2019.00181

10.1109/CVPR46437.2021.01458

10.1007/978-3-030-58595-2_30