[4] Protter M, Elad M. Image sequence denoising via sparse and redundant representations [J]. IEEE Transaction on Image Processing, 2009, 18(1):27-35.
[6] Ye J P. Generalized low rank approximations of matrices [J]. Machine Learning, 2005, 61(1):167-191.
[7] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries [J]. TIP, 2006,15(12):3736-3745.
[8] Ji H, Liu C, Shen Z, et al. Robust video denoising using low rank matrix completion: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition [C]. San Francisco, 2010.
[9] Gu S, Zhang L, Zuo W, et al. Weighted nuclear norm minimization with application to image denoising: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition [C]. Columbus, 2014.
[10] Yin K, Lei Z, Zhang Y. An adaptive rank-sparsity K-SVD algorithm for image sequence denoising [J]. Pattern Recognition Letters, 2014, 45:46-54.
[11] Furnival T, Leary R K, Midgley P A. Denoising time-resolved microscopy image sequences with singular value thresholding [J]. Ultramicroscopy, 2017, 178(1):112-124.
[12] Gu S H, Xie Q, Meng D Y, et al. Weighted nuclear norm minimization and its applications to low level vision [J]. International Journal of Computer Vision, 2017, 121:183-208.
[13] Garceau S: TDP MARS LW K508 [R]. France: Sofradir Corporation, 2013.
[15] Recht B, Fazel M, Parrilo P A. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization [J]. SIAM Review, 2010, 52(3):471-501.
[16] Cai J F, Candès E J, Shen Z. A singular value thresholding algorithm for matrix completion [J]. SIAM J. Optim, 2010, 20(4):1956-1982.
[17] Candèes E J, Li X D, Ma Y, et al. Robust principal component analysis [J]. Journal of the ACM, 2011, 58(3):1-37.
[18] Zhang Z D, Ganesh A, Liang X, et al. TILT: transform invariant low-rank textures [J]. International Journal of Computer Vision, 2012, 99(1):1-24.