An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34048-2_31
Reference34 articles.
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3. Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-GAN. Adv. Neural Inf. Process. Syst. 32, 284–293 (2019)
4. Block, K.T., Uecker, M., Frahm, J.: Suppression of MRI truncation artifacts using total variation constrained data extrapolation. Int. J. Biomed. Imaging 2008, 1–8 (2008). https://doi.org/10.1155/2008/184123, http://www.hindawi.com/journals/ijbi/2008/184123/
5. Chen, S., et al.: Unsupervised image super-resolution with an indirect supervised path. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)
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