A Direct Algorithm for 1-D Total Variation Denoising
By Laurent Condat. IEEE Signal Processing Letters, 2013
368 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/38a3e5e5f2385971c046366f3539c1bc6576e63f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Thomas Pock
- Paolo Favaro
- Antonin Chambolle
- Ming Yan
- Daniel J. Perrone
- Alexander Mielke
- V. Caselles
- Gianni Dal Maso
In Computational Mechanics
- Ming Yan
- Thomas Pock
- Antonin Chambolle
- Laurent Condat
- Paolo Favaro
- Daniel J. Perrone
- Peter Richtárik
- Francis R. Bach
In Computer Vision and Pattern Recognition
- Ming Yan
- Thomas Pock
- Luc Van Gool
- Ming-Hsuan Yang
- Lei Zhang
- Antonin Chambolle
- Jitendra Malik
- Dacheng Tao
Citations in the corpus
- An introduction to continuous optimization for imaging (2016, methodology): “L. Condat (2013a), ‘A direct algorithm for 1D total variation denoising’, IEEE Signal Proc.”
- A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator (2016, positive): “For example, the proximal mapping of μ2‖Dx‖1 can be computed exactly and quickly [11], and Davis–Yin can be applied directly without using the primal–dual algorithms.”
- Total Variation Blind Deconvolution: The Devil Is in the Details (2014, methodology): “The proof exploits recent work of Condat [5], Strong and Chan [20] and the taut string algorithm of Davies and Kovacs [6].”