[1] S. Xue, R. Bai, and X. Jin. 2D probabilistic undersampling pattern optimization for MR image reconstruction. arXiv preprint, 2020. arXiv:2003.03797
[2] S. Xue, W. Qiu, F. Liu, and X. Jin. Wavelet-based residual attention network for image super-resolution. Neurocomputing, 2020, 382:116-126. DOI 10.1016/j.neucom.2019.11.044
[3] S. Xue, W. Qiu, F. Liu, and X. Jin. Faster super-resolution by improved frequency domain neural networks. Singal, Image and Video Processing, 2020, 14:257–265. DOI 10.1007/s11760-019-01548-8
Fig. 5 Illustration of the t-SVD of an n1×n2×n3n1×n2×n3 tensor, i.e., A=U∗S∗VTA=U∗S∗VT. Our tensor nuclear norm ∥A∥∗‖A‖∗ is defined as the sum of singular values of all frontal slices of the f-diagonal SS, i.e., ∥A∥∗≜tr(S)=∑n3i=1tr(S(i))=tr(S¯(1))=∥A¯(1)∥∗‖A‖∗≜tr(S)=∑i=1n3tr(S(i))=tr(S¯(1))=‖A¯(1)‖∗. Note that our tensor nuclear norm becomes standard matrix nuclear norm when n3=1n3=1. Thus, our tensor nuclear norm can be considered as a direct extension from the matrix case to the tensor case.
[4] S. Xue, W. Qiu, F. Liu, and X. Jin. Low-rank tensor completion by truncated nuclear norm regularization. 24th International Conference on Pattern Recognition, Beijing, 2018, p.2600-2605. DOI 10.1109/ICPR.2018.8546008
[5] S. Xue and X. Jin. Robust classwise and projective low-rank representation for image classification. Signal, Image and Video Processing, 2018, 12(1):107-115. DOI 10.1007/s11760-017-1136-1
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Created on Tuesday, 13 March 2018. Last modified on Sunday, 2 August 2020.