@Article{JICS-1-29,
author = {},
title = {Predictor-Corrector Method for Total Variation Based Image Denoising},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {1},
number = {1},
pages = {29--36},
abstract = { Since  their  introduction  in  a  classic  paper  by  Rudin,  Osher  and  Fetemi  [1],  total  variation 
minimizing models have become one of the most popular and successful methodology for image restoration. 
More recently, there has been a resurgence of interest and exciting new developments, some extending the 
applicabilities to inpainting, blind deconvolution and vector-valued images, while others offer improvements 
in  better  preservation  of  contrast,  geometry  and  textures,  in  ameliorating  the  staircasing  effect,  and  in 
exploiting the multiscale nature of the models. In addition, new computational methods have been proposed 
with  improved  computational  speed  and  robustness.  In  this  paper,  a  predictor-Corrector  techniques  are 
pointed out and applied in to the total variation-based image denoising.The numerical experiments shows the 
improvement are fairly valid. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22857.html}
}