期刊文献+

一种新的Bregman迭代方法在信号恢复中的应用

Application of a New Bregman Iterative Algorithm in Signal Recovery
下载PDF
导出
摘要 本文结合残量Bregman迭代方法以及不动点迭代方法提出一种新迭代方法,将其应用于信号恢复问题.数值试验表明,新方法避免了Bregman迭代方法产生的停滞现象且较线性Bregman迭代方法更稳定、快速、有效. In this paper,based on the residual Bregman iterative algorithm and fixed point iteration,we propose a novel Bregman iterative algorithm. The novel algorithm is then applied to signal recovery. Numerical tests indicate the novel algorithm can avoid the stagnation and is more rapid,stable and efficient than the linearized Bregman algorithm.
出处 《数学理论与应用》 2015年第1期115-121,共7页 Mathematical Theory and Applications
关键词 Bregman迭代 不动点迭代 信号恢复 Bregman iterative Fixed point iteration Signal recovery
  • 相关文献

参考文献10

  • 1D. L. Donoho. Denoiding by soft-thresholding[J]. IEEE Trans. Inform. Theory, 1995, 3:613-627. 被引量:1
  • 2Darbon, S. Osher. Fast discrete optimization for sparse approximations and deconvolutions [ J ]. Preprint, UCLA, 2007. 被引量:1
  • 3S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin. An iterative regularization method for variation - based image restoration[J]. Muitiscale Model. Simul,2005, 4(2) : 460 -489. 被引量:1
  • 4W. Yin, S. Osher, D. Goldfarb and J. Darbon. Bregman iterative algorithms for 11 - minimization with applica- tions to compressed sensing[J]. SIAM J. Imaging Sci, 2008, 1 (1) : 143 - 168. 被引量:1
  • 5J.F. Cai, S. Osher, and Z. Shen. Linearized Bregman iterations for compressed sensing [ J ], Math. Comp, 2009, 78:1515-1536. 被引量:1
  • 6李娟,李维国,郑昭静.求解稀疏最小二乘问题的新型Bregman迭代正则化算法[J].信号处理,2012,28(8):1164-1170. 被引量:4
  • 7C.R. Vogel and M.E. Oman. Iterative methods for total variation denoising. SIAM J. Sci. Comput, 1996, 17 (1) : 227 -238. 被引量:1
  • 8Gene H. Golub, Charles F. Van Loan. Matrix Computations [ M ]. Beijing: Posts & Telecom Press, 2009 : 88 - 140. 被引量:1
  • 9郑昭静,李维国.基于Bregman迭代的稀疏最小二乘问题的预测校正法[J].高等学校计算数学学报,2014,36(2):167-182. 被引量:2
  • 10S. Osher, Y. Mao, B. Dong, and W. Yin. Fast linearized Bregman iteration for compressive sensing and sparse denoising. Commun. Math. Sci, 2010, 8 ( 1 ) : 93 - 111. 被引量:1

二级参考文献25

  • 1Chen S. S. , Donoho D. L. , et al. Atomic decomposition by basis pursuit [ J ]. SIAM J. Sci. Comput, 1998, 20: 33-61. 被引量:1
  • 2Candes E. , Romberg J. and Tao T.. Robust uncertainty principles:exact signal reconstruction from highly incom- plete frequency information [J] . IEEE Trans. Inform. Theory, 2006,52 : 489 -509. 被引量:1
  • 3Donoho D. L.. Compressed Sensing [ J]. IEEE Trans. Inform. Theory ,2006,52 : 1289-1306. 被引量:1
  • 4Hale E. ,Yin W. and Zhang Y.. A Fixed-Point Continua- tion Method for LI-Regularization with Application to Compressed Sensing [ R ]. CAAM Technical report tr07- 07, Rice University, Houston,TX,2007. 被引量:1
  • 5Yin W. ,Osher S. ,et al. Bregman iterative 'algorithms for l-Regularization with Application to Compressed Sensing J]. SIAM J. Imaging Sciences,2008,1:143-168. 被引量:1
  • 6Cai J. F. , Chan R. H. , et al. Linearizad Bregman itera- tions for compresses sensing [ J ]. Math. Comp. , 2009,78 (267) :1515-1536. 被引量:1
  • 7Cai J. F. , Osher S. , et al. Linearized Bregman Iteration for Frame-Based Image Deblurring [ J ]. SIAM J. Imaging Sciences ,2009,2( 1 ) :226-252. 被引量:1
  • 8Ben-Israel A. and Greville T. N. E.. Generalized inverses: Theory and Applications (M). 2nd ed. New York, NY: Springer,2003:35-130. 被引量:1
  • 9Osher S. , Mao Y. , et al. Fast Linearized Bregman Itera- tion for Compressed Sensing and Sparse Denoising [ R ]. Report 08-37, UCEA : CAM ,2008 : 1-18. 被引量:1
  • 10Donoho D. L... Denoising by soft-thresholding [ J ]. IEEE Trans. Inform. Theory,1995,3:613-627. 被引量:1

共引文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部