Joint restoration convolutional neural network for low-quality image super resolution
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-020-01998-z.pdf
Reference41 articles.
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2. Amaranageswarao, G., Deivalakshmi, S., Ko, S.-B.: Residual learning based densely connected deep dilated network for joint deblocking and super resolution. Appl. Intell. (2020). https://doi.org/10.1007/s10489-020-01670-y
3. Bevilacqua, M., Roumy, A., Guillemot, C.: line Alberi Morel M (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, BMVA Press, pp 135.1–135.10, https://doi.org/10.5244/C.26.135
4. Cavigelli, L., Hager, P., Benini, L.: Cas-cnn: A deep convolutional neural network for image compression artifact suppression. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp 752–759 (2017) https://doi.org/10.1109/IJCNN.2017.7965927
5. Chen, H., He, X., Ren, C., Qing, L., Teng, Q.: Cisrdcnn: super-resolution of compressed images using deep convolutional neural networks. Neurocomputing 285, 204–219 (2018). https://doi.org/10.1016/j.neucom.2018.01.043
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