@Article{jcs.2020.06429,
AUTHOR = {Jianwei Zhang, Ze Qin, Shunfeng Wang},
TITLE = {A New Adaptive Regularization Parameter Selection Based on  Expected Patch Log Likelihood},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {25--36},
URL = {http://www.techscience.com/JCS/v2n1/39370},
ISSN = {2579-0064},
ABSTRACT = {Digital images have been applied to various areas such as evidence in courts. 
However, it always suffers from noise by criminals. This type of computer network 
security has become a hot issue that can’t be ignored. In this paper, we focus on noise 
removal so as to provide guarantees for computer network security. Firstly, we introduce 
a well-known denoising method called Expected Patch Log Likelihood (EPLL) with 
Gaussian Mixture Model as its prior. This method achieves exciting results in noise 
removal. However, there remain problems to be solved such as preserving the edge and 
meaningful details in image denoising, cause it considers a constant as regularization 
parameter so that we denoise with the same strength on the whole image. This leads to a 
problem that edges and meaningful details may be oversmoothed. Under the 
consideration of preserving edges of the image, we introduce a new adaptive parameter 
selection based on EPLL by the use of the image gradient and variance, which varies with 
different regions of the image. Moreover, we add a gradient fidelity term to relieve 
staircase effect and preserve more details. The experiment shows that our proposed 
method proves the effectiveness not only in vision but also on quantitative evaluation.},
DOI = {10.32604/jcs.2020.06429}
}