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副教授   博士生导师   硕士生导师

性别: 男

毕业院校: 中国科学院大学

学位: 博士

所在单位: 信息与通信工程学院

联系方式: zhaowenda@dlut.edu.cn

电子邮箱: zhaowenda@dlut.edu.cn

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Local binary pattern metric-based multi-focus image fusion

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论文类型: 期刊论文

发表时间: 2019-02-01

发表刊物: OPTICS AND LASER TECHNOLOGY

收录刊物: SCIE、Scopus

卷号: 110

期号: ,SI

页面范围: 62-68

ISSN号: 0030-3992

关键字: Multi-focus image fusion; Local binary pattern; Connected area judgment strategy

摘要: Multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. Nowadays, it has become an important research topic due to the applications in more and more scientific fields. However, preserving more information of the low-contrast area in the focus area and maintaining the edge information are two challenges for existing approaches. In this paper, we address these two challenges with presenting a simple yet efficient multi-focus fusion method based on local binary pattern (LBP). In our algorithm, we measure the clarity using the LBP metric and construct the initial weight map. And then we use the connected area judgment strategy (CADS) to reduce the noise in the initial map. Afterwards, the two source images are fused together by weighted arranging. The experimental results validate that the proposed algorithm outperforms state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations, especially when dealing with low contrast regions and edge information. (C) 2018 Elsevier Ltd. All rights reserved.

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