Single MR-image super-resolution based on convolutional sparse representation
Author:
Funder
Babol Noshirvani University of Technology
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-020-01698-0.pdf
Reference49 articles.
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3. Huang, S., Sun, J., Yang, Y., Fang, Y., Lin, P., Que, Y.: Robust single-image super-resolution based on adaptive edge-preserving smoothing regularization. IEEE Trans. Image Process. 27(6), 2650–2663 (2018)
4. Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569–2582 (2014)
5. Tang, S., Guo, H., Zhou, N., Huang, L., Zhan, T.: Coupled dictionary learning on common feature space for medical image super resolution. In: IEEE Conference on Image Processing (ICIP), pp. 574–578 (2016)
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