Local binary pattern metric-based multi-focus image fusion. (February 2019)

• Record Type: Journal Article
• Authors: Yin, Weiling; Zhao, Wenda; You, Di; Wang, Dong
• Is Part Of: Optics & laser technology. Volume 110(2019)
• Page Start: 62
• Page End: 68
• Publication Date: 2019-02
• Subjects: Multi-focus image fusion -- Local binary pattern -- Connected area judgment strategy; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2018.07.045
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: An efficient multi-focus fusion method based on local binary patterns is proposed. The frequent loss of low-contrast area information is successfully avoided. The proposed algorithm outperforms state-of-the-art image fusion algorithms. Abstract: 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 (CAJS) 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.