中图分类号:
68U10
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参考文献
[1] 史加荣, 郑秀云, 魏宗田, 等.低秩矩阵恢复算法综述.计算机应用研究, 2013, 30(6):1601-1605. (Shi J R, Zheng X Y, Wei Z T, et al. Overview of low-rank matrix recovery algorithms. Application Research of Computers, 2013, 30(6):1601-1605.)
[2] Hale E T, Yin W, Zhang Y. Fixed-point continuation method for l1-minimization:Methodology and convergence. SIAM Journal on Optimization, 2008, 19(3):1107-1130.
[3] Bouwmans T, Javed S, Zhang H, et al. On the applications of robust PCA in image and video processing. Proceedings of the IEEE, 2018, 106(8):1427-1457.
[4] Bouwmans T, Sobral A, Javed S, et al. Decomposition into low-rank plus additive matrices for background/foreground separation:A review for a comparative evaluation with a large-scale dataset. Computer Science Review, 2017, 23:1-71.
[5] Candes E J, Li X, Ma Y, et al. Robust principal component analysis?. Journal of the ACM, 2011, 58(3):1-37.
[6] 肖萌.改进的鲁棒主成分分析模型及其应用.硕士论文.重庆大学, 重庆, 2016. (Xiao M. Improved robust principal component analysis model and its application. Master Thesis. Chongqing University, Chongqing, 2016.)
[7] Lin Z, Ganesh A, Wright J, et al. Fast convex optimization algorithms for exact recovery of exact recovery of a corrupted low-rank matrix. UIUC Technical Report UILU-ENG-09-2214, 2009.
[8] Lin Z, Chen M, Ma Y. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXivPreprint arXiv:1009.5055, 2010.
[9] Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 2011, 3(1):1-122.
[10] 杨国亮, 王艳芳, 丰义琴, 等.基于加权RPCA的非局部图像去噪方法.计算机工程与设计, 2015, 36(11):3035-3040. (Yang G L, Wang Y F, Feng Y Q, et al. Non-local image denoising method based on weighted RPCA. Computer Engineering and Design, 2015, 36(11):3035-3040.)
[11] Gu S, Zhang L, Zuo W, et al. Weighted nuclear norm minimization with application to image denoising. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014, 2862-2869.
[12] 李吉.低秩矩阵恢复算法的改进.硕士论文.北京化工大学, 北京, 2017. (Li J. Improvement of low rank matrix recovery algorithm. Master Thesis. Beijing University of Chemical Technology, Beijing, 2017.)
[13] 史加荣, 李雪霞.基于矩阵补全的气象数据推测.气象科技, 2019, 47(3):420-425. (Shi J R, Li X X. Prediction of meteorological data based on matrix completion. Meteorological Science and Technology, 2019, 47(3):420-425.)
[14] 李慧玲.鲁棒加权核范数的图像去噪方法.硕士论文.辽宁师范大学, 大连, 2018. (Li H L. Image denosing with robust weighted kernel norm. Master Thesis. Liaoning Normal University, Dalian, 2018.)
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脚注
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基金
国家自然科学基金(11861026),湖北省冶金工业过程重点实验室(Y201905),广西自然科学基金(2021GXNSFAA220034),湖北省教育厅科学研究计划资助项目青年项目(Q20211111)资助课题.
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