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Volume 5, Issue 3
Image Denoising using LIT Model and Iterated Total Variation Refinement

FENLIN YANG, KE CHEN, BO YU, AND ZHIGANG YAN

Int. J. Numer. Anal. Mod. B, 5 (2014), pp. 255-268

Published online: 2014-05

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  • Abstract
Developing a variational model that is capable of restoring both smooth (no edges) and non-smooth (with edges) images is still a valid challenge in the image processing. In this paper, we present two methods for image denoising problems based on the use of the LLT model (see [14]) and iterated total variation refinement. The idea of our methods is, first make use of the LLT model to get a smooth primal sketch, and then get some meaningful signal by iterated total variation refinement from the removed noise image. Numerical experiments show that our method is able to maintain some important information such as small details in the image, and at the same time to get a better visualization.
  • Keywords

Image denoising staircasing effect primal sketch hierarchical decomposition iterated regularization

  • AMS Subject Headings

35R35 49J40 60G40

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAMB-5-255, author = {FENLIN YANG, KE CHEN, BO YU, AND ZHIGANG YAN}, title = {Image Denoising using LIT Model and Iterated Total Variation Refinement}, journal = {International Journal of Numerical Analysis Modeling Series B}, year = {2014}, volume = {5}, number = {3}, pages = {255--268}, abstract = {Developing a variational model that is capable of restoring both smooth (no edges) and non-smooth (with edges) images is still a valid challenge in the image processing. In this paper, we present two methods for image denoising problems based on the use of the LLT model (see [14]) and iterated total variation refinement. The idea of our methods is, first make use of the LLT model to get a smooth primal sketch, and then get some meaningful signal by iterated total variation refinement from the removed noise image. Numerical experiments show that our method is able to maintain some important information such as small details in the image, and at the same time to get a better visualization.}, issn = {}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnamb/233.html} }
TY - JOUR T1 - Image Denoising using LIT Model and Iterated Total Variation Refinement AU - FENLIN YANG, KE CHEN, BO YU, AND ZHIGANG YAN JO - International Journal of Numerical Analysis Modeling Series B VL - 3 SP - 255 EP - 268 PY - 2014 DA - 2014/05 SN - 5 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnamb/233.html KW - Image denoising KW - staircasing effect KW - primal sketch KW - hierarchical decomposition KW - iterated regularization AB - Developing a variational model that is capable of restoring both smooth (no edges) and non-smooth (with edges) images is still a valid challenge in the image processing. In this paper, we present two methods for image denoising problems based on the use of the LLT model (see [14]) and iterated total variation refinement. The idea of our methods is, first make use of the LLT model to get a smooth primal sketch, and then get some meaningful signal by iterated total variation refinement from the removed noise image. Numerical experiments show that our method is able to maintain some important information such as small details in the image, and at the same time to get a better visualization.
FENLIN YANG, KE CHEN, BO YU, AND ZHIGANG YAN. (2014). Image Denoising using LIT Model and Iterated Total Variation Refinement. International Journal of Numerical Analysis Modeling Series B. 5 (3). 255-268. doi:
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