Open Access
Issue
MATEC Web Conf.
Volume 370, 2022
2022 RAPDASA-RobMech-PRASA-CoSAAMI Conference - Digital Technology in Product Development - The 23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI
Article Number 07011
Number of page(s) 20
Section Pattern Recognition
DOI https://doi.org/10.1051/matecconf/202237007011
Published online 01 December 2022
  1. Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super- resolution: Dataset and study. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1122–1131, 2017. [Google Scholar]
  2. Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel. Low- complexity single-image super-resolution based on nonnegative neighbor embedding. 2012. [Google Scholar]
  3. Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos. To learn image super-resolution, use a GAN to learn how to do image degradation first. CoRR, abs/1807.11458, 2018. [Google Scholar]
  4. Honggang Chen, Xiaohai He, Linbo Qing, Yuanyuan Wu, Chao Ren, Ray E. Sheriff, and Ce Zhu. Real-world single image super-resolution: A brief review. Information Fusion, 79:124–145, 2022. [Google Scholar]
  5. Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks, 2015. [Google Scholar]
  6. Chao Dong, Chen Change Loy, and Xiaoou Tang. Accelerating the super-resolution con- volutional neural network, 2016. [Google Scholar]
  7. Chao Dong, Chen Change Loy, and Xiaoou Tang. Accelerating the super-resolution con- volutional neural network. CoRR, abs/1608.00367, 2016. [Google Scholar]
  8. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sher- jil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks, 2014. [Google Scholar]
  9. Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong. Blind super-resolution with iterative kernel correction. CoRR, abs/1904.03377, 2019. [Google Scholar]
  10. Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong. Blind super-resolution with iterative kernel correction, 2019. [Google Scholar]
  11. Shuhang Gu, Wangmeng Zuo, Qi Xie, Deyu Meng, Xiangchu Feng, and Lei Zhang. Convo- lutional sparse coding for image super-resolution. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015. [Google Scholar]
  12. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015. [Google Scholar]
  13. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. [Google Scholar]
  14. Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio. Denoising cri- terion for variational auto-encoding framework, 2016. [Google Scholar]
  15. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network train- ing by reducing internal covariate shift, 2015. [Google Scholar]
  16. Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution, 2016. [Google Scholar]
  17. Alexia Jolicoeur-Martineau. The relativistic discriminator: a key element missing from standard gan, 2018. [Google Scholar]
  18. Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Deeply-recursive convolutional network for image super-resolution, 2016. [Google Scholar]
  19. Claude Knaus and Matthias Zwicker. Progressive image denoising. Image Processing, IEEE Transactions on, 23:3114–3125, 07 2014. [CrossRef] [Google Scholar]
  20. D. Kouame and M. Ploquin. Super-resolution in medical imaging: An illustrative approach through ultrasound. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pages 249–252, 2009. [Google Scholar]
  21. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, L´eon Bottou, and Kilian Q. Weinberger, editors, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3–6, 2012 , Lake Tahoe, Nevada, United States, pages 1106–1114, 2012. [Google Scholar]
  22. M. Lebrun, M. Colom, A. Buades, and J. M. Morel. Secrets of image denoising cuisine. Acta Numerica, 21:475–576, 2012. [CrossRef] [Google Scholar]
  23. Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Ale- jandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. Photo-realistic single image super-resolution using a generative adversarial network. 2017. [Google Scholar]
  24. David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 2, pages 416–423. IEEE, 2001. [Google Scholar]
  25. Michael Mathieu, Camille Couprie, and Yann LeCun. Deep multi-scale video prediction beyond mean square error, 2016. [Google Scholar]
  26. Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin. Pulse: Self-supervised photo upsampling via latent space exploration of generative models, 2020. [Google Scholar]
  27. Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral nor- malization for generative adversarial networks. CoRR, abs/1802.05957, 2018. [Google Scholar]
  28. Wenzhe Shi, Jose Caballero, Ferenc Husz´ar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. CoRR, abs/1609.05158, 2016. [Google Scholar]
  29. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition, 2015. [Google Scholar]
  30. Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang. ESRGAN: enhanced super-resolution generative adversarial networks. CoRR, abs/1809.00219, 2018. [Google Scholar]
  31. Zhihao Wang, Jian Chen, and Steven C. H. Hoi. Deep learning for image super-resolution: A survey. 2020. [Google Scholar]
  32. Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, and Thomas S. Huang. Wide activation for efficient and accurate image super-resolution. CoRR, abs/1808.08718, 2018. [Google Scholar]
  33. Linwei Yue, Huanfeng Shen, Jie Li, Qiangqiang Yuan, Hongyan Zhang, and Liangpei Zhang. Image super-resolution: The techniques, applications, and future. Signal Processing, 128:389–408, 2016. [CrossRef] [Google Scholar]
  34. Roman Zeyde, Michael Elad, and Matan Protter. On single image scale-up using sparse- representations. In International conference on curves and surfaces, pages 711–730. Springer, 2010. [Google Scholar]
  35. Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. CoRR, abs/1608.03981, 2016. [Google Scholar]
  36. Kai Zhang, Wangmeng Zuo, and Lei Zhang. Learning a single convolutional super-resolution network for multiple degradations, 2018 [Google Scholar]
  37. Shaolei Zhang, Guangyuan Fu, Hongqiao Wang, and Yuqing Zhao. Degradation learn- ing for unsupervised hyperspectral image super-resolution based on generative adversarial network. Signal, Image and Video Processing, 15:1–9, 11 2021. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.