full
search.png

Search

close.png

Cancel

arrow
Volume 5, Issue 2
A Primal-Dual Hybrid Gradient Algorithm to Solve the LLT Model for Image Denoising

Chunxiao Liu, Dexing Kong & Shengfeng Zhu

DOI: 10.4208/nmtma.2012.m1047

Numer. Math. Theor. Meth. Appl., 5 (2012), pp. 260-277.

Published online: 2012-05

Preview Full PDF 2657 32351

Cited by

google scholar semantic scholar
Export citation
  • Abstract

We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images. Based on the primal-dual formulation of the original nondifferentiable model, the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows. Numerical examples are provided to demonstrate the superiority of our algorithm.

  • Keywords

LLT model, image denoising, primal-dual.

  • AMS Subject Headings

68U10, 65K10

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{NMTMA-5-260, author = {Chunxiao Liu, Dexing Kong and Shengfeng Zhu}, title = {A Primal-Dual Hybrid Gradient Algorithm to Solve the LLT Model for Image Denoising}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2012}, volume = {5}, number = {2}, pages = {260--277}, abstract = {

We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images. Based on the primal-dual formulation of the original nondifferentiable model, the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows. Numerical examples are provided to demonstrate the superiority of our algorithm.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2012.m1047}, url = {http://global-sci.org/intro/article_detail/nmtma/5938.html} }
TY - JOUR T1 - A Primal-Dual Hybrid Gradient Algorithm to Solve the LLT Model for Image Denoising AU - Chunxiao Liu, Dexing Kong & Shengfeng Zhu JO - Numerical Mathematics: Theory, Methods and Applications VL - 2 SP - 260 EP - 277 PY - 2012 DA - 2012/05 SN - 5 DO - http://doi.org/10.4208/nmtma.2012.m1047 UR - https://global-sci.org/intro/article_detail/nmtma/5938.html KW - LLT model, image denoising, primal-dual. AB -

We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images. Based on the primal-dual formulation of the original nondifferentiable model, the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows. Numerical examples are provided to demonstrate the superiority of our algorithm.

Chunxiao Liu, Dexing Kong and Shengfeng Zhu. (2012). A Primal-Dual Hybrid Gradient Algorithm to Solve the LLT Model for Image Denoising. Numerical Mathematics: Theory, Methods and Applications. 5 (2). 260-277. doi:10.4208/nmtma.2012.m1047
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard