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제목 (J-KSIAM) Volume 26 Number 3 (September 2022 issue) TOC
작성자 KSIAM 등록일 2022-09-28
이메일 ksiam@ksiam.org

Dear colleagues and researchers,

 

The Journal of the Korean Society for Industrial and Applied Mathematics (J-KSIAM) Volume 26 Number 3 (September 2022 issue) has been posed on KSIAM Journal Archive or other information on the journal is available on the KSIAM website http://www.ksiam.org or https://ksiam.org/journal/journal01.asp.

The journal is one of Korea Citation Indexed (KCI) journals since 2007 and is indexed in Emerging Sources Citation Index (ESCI) since 2017. 

Readers interested in the following articles may download each of articles free of charge from our website and authors are encouraged to submit a paper via the online submission site.

 

Sincerely yours,

Jae Hoon Jung, Editor-in-Chief

Min Seok Choi, Gun Jin Yun, Managing Editors


 
JKSIAM-v26n3 pp138-155
 
AN IMPROVED IMPLICIT Euler method FOR SOLVING INITIAL VALUE PROBLEMS
 
BEONG IN YUN
 
 
To solve the initial value problem we present a new single-step implicit method based on the Euler method. We prove that the proposed method has convergence order 2. In practice, numerical results of the proposed method for some selected examples show an error tendency similar to the second-order Taylor method. It can also be found that this method is useful for stiff initial value problems, even when a small number of nodes are used. In addition, we extend the proposed method by using weighted averages with a parameter and show that its convergence order becomes 2 for the parameter near 1/2. Moreover, it can be seen that the extended method with properly selected values of the parameter
improves the approximation error more significantly.
 
 
JKSIAM-v26n3 pp156-184
 
SATURATION-VALUE TOTAL VARIATION BASED COLOR IMAGE DENOISING UNDER MIXED MULTIPLICATIVE AND GAUSSIAN NOISE
 
MIYOUN JUNG
 
 
In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.
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