Title :
A Direct Algorithm for 1-D Total Variation Denoising
Author_Institution :
GIPSA-Lab., Univ. of Grenoble, Grenoble, France
Abstract :
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.
Keywords :
least squares approximations; signal denoising; 1D total variation denoising; C code implementation; direct algorithm; one-dimensional discrete signal smoothing; related fused lasso problem; total variation regularized least-square problem; very-fast-noniterative algorithm; Electron tubes; Iterative methods; Noise reduction; Optimization; Signal processing algorithms; Smoothing methods; TV; Convex nonsmooth optimization; denoising; fused lasso; nonlinear smoothing; nonparametric regression; regularized least-squares; taut string; total variation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2278339
