Earticle

초록

영어

Multi-focus image fusion means to fuse multiple source images with different focus settings into one image, so that the resulting image appears sharper. In order to extract the focused regions of the fused image efficiently, a novel pulse coupled neural network (PCNN) method for multi-focus image fusion is proposed. The registered source images are decomposed into principal components and sparse components by robust principal component analysis (RPCA) decomposition, and the important features of the sparse components are used to motivate the PCNN neurons, whose outputs detect the focused regions of the source images and integrate them to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in terms of mutual information (MI) and QAB/F
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목차

Abstract
 1. Introduction
 2. Pulse Coupled Neural Network
 3. Proposed Method
 4. Experimental Results
  4.1. Qualitative Analysis
  4.2. Quantitative Analysis
 5. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Yongxin Zhang [ School of Information Science and Technology, Northwest University, Xi’an, 710127, China, Luoyang Normal University, Luoyang, 471022, China ]
• Li Chen [ School of Information Science and Technology, Northwest University, Xi’an, 710127, China ]
• Zhihua Zhao [ School of Information Science and Technology, Northwest University, Xi’an, 710127, China ]
• Jian Jia [ Department of Mathematics, Northwest University, Xi’an, 710127, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605