Reconstructing images corrupted by noise based on D-S evidence theory

被引：6
|
作者：
Zhao, Ye [1 ,2 ]
Mi, Ju-sheng [1 ]
Liu, Xin [3 ]
Sun, Xiao-yun [2 ]
机构：
[1] Hebei Normal Univ, Coll Math & Informat Sci, Shijiazhuang 050016, Hebei, Peoples R China
[2] Shijiazhuang Tie Dao Univ, Dept Math & Phys, Shijiazhuang 050043, Hebei, Peoples R China
[3] Chengde Petr Coll, Dept Math & Phys, Chengde 067000, Hebei, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2017年 / 8卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Image noise; Evidence theory; D-S rule; DEMPSTER-SHAFER THEORY; CLASSIFICATION; FUSION;
D O I：
10.1007/s13042-015-0353-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new algorithm of noise reduction for image based on evidence theory is proposed. The values of all pixels are restricted in interval [0, 1], and set of data in each column is a term of mass function, which can be calculated by D-S composition rule. Judging noise can be achieved by comparing with the value of pixel in middle and of the current one. The noise will be removed by substituting the current value with value computed. An improved accelerated algorithm is also presented by sample window of 2 x 2. As a measure of conflict K with greater value shows that there would be noises within the current sample window. At last, Experiment image "Lena" with additive noise shows as a test sample, that better result can be achieved with the algorithm.
引用
收藏
页码：611 / 618
页数：8
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