@Article{NMTMA-2-4,
author = {},
title = {A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2009},
volume = {2},
number = {4},
pages = {353--376},
abstract = {<p style="text-align: justify;">In this paper, we propose a compound algorithm for the image restoration.
The algorithm is a convex combination of the ROF model and the LLT model with a
parameter function $\theta$. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular,
the errors of the compound algorithm in $L_2$ norm between the exact images and corresponding restored images are the smallest among the three models. For images with
strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method,
an improved AMG method and the Krylov acceleration. It is found that the combination
of these methods is efficient and robust in the image restoration.<br/></p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2009.m9001s},
url = {https://global-sci.com/article/90757/a-compound-algorithm-of-denoising-using-second-order-and-fourth-order-partial-differential-equations}
}