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Local Binary Pattern Metric-Based Multi-focus Image Fusion

  • Wenda ZhaoEmail author
  • Weiling Yin
  • Di You
  • Dong Wang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

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.

Keywords

Multi-focus image fusion Local binary pattern Connected area judgment strategy Preserving the low-contrast area information Maintaining the edge information 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina

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