A Direct Algorithm for 1-D Total Variation Denoising

被引：243
作者：
Condat, Laurent [1 ]
机构：
[1] GIPSA Lab, Grenoble, France
来源：
IEEE SIGNAL PROCESSING LETTERS | 2013年 / 20卷 / 11期
关键词：
Convex nonsmooth optimization; denoising; fused lasso; nonlinear smoothing; nonparametric regression; regularized least-squares; taut string; total variation; OPTIMIZATION; MINIMIZATION; EQUIVALENCE; SPARSITY;
D O I：
10.1109/LSP.2013.2278339
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional discrete signals, by solving the total variation regularized least-squares problem or the related fused lasso problem. A C code implementation is available on the web page of the author.
引用
收藏
页码：1054 / 1057
页数：4
相关论文
共 38 条
  • [1] [Anonymous], MATH MODELS COMPUTER
  • [2] [Anonymous], 2011, PROC 28 INT C MACH L
  • [3] Optimization with Sparsity-Inducing Penalties
    Bach, Francis
    Jenatton, Rodolphe
    Mairal, Julien
    Obozinski, Guillaume
    [J]. FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2012, 4 (01): : 1 - 106
  • [4] Bauschke HH, 2011, CMS BOOKS MATH, P1, DOI 10.1007/978-1-4419-9467-7
  • [5] Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
    Beck, Amir
    Teboulle, Marc
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009, 18 (11) : 2419 - 2434
  • [6] Bleakley K., 2011, PUBLICATION IN PRESS
  • [7] Chambolle A, 2005, LECT NOTES COMPUT SC, V3757, P136, DOI 10.1007/11585978_10
  • [8] Chambolle A., 2009, INT J COMPUT VIS, V84
  • [9] Chambolle A., 2012, IMAGE PROCESSING ANA
  • [10] Chambolle A., 2010, Theoretical foundations and numerical methods for sparse recovery, V9, P227
1234