@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} }