Effective Utilization of Hybrid Residual Modules in Deep Neural Networks for Super Resolution
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-37734-2_64
Reference24 articles.
1. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)
2. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
3. Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: The European Conference on Computer Vision (ECCV), September 2018
4. Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017
5. Muqeet, A., Iqbal, M.T., Bae, S.H.: Hybrid Residual Attention Network for Single Image Super Resolution. arXiv preprint arXiv:1907.05514 2019
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