Earticle

초록

영어

After the image decomposition with Non-subsampled Shearlet Transform (NSST), the transform coefficients have a larger redundancy. In order to reduce the redundant information, an image fusion algorithm based on NSST and non-negative matrix factorization (NMF) is introduced. The source images are decomposed with NSST into the low-frequency coefficients and the high-frequency sub-band coefficients. The low-frequency coefficients are fused based on NMF theory. The high-frequency coefficients are fused based on Regional Sum Modified-Laplacian (SML) Maximum. Finally, the inverse NSST is used to reconstruct the final fused image. The proposed algorithm can effectively remove redundant information, extract global features and capture more direction details information of multi-source image. Experiments show that the proposed algorithm has obvious advantages and the fused image quality has been greatly improved. The presented algorithm is superior to other fusion algorithms from the objective parameters.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Non-subsampled Shearlet Transform
  2.2. Theory of Non-negative Matrix Factorization
 3. An Algorithm of Medical Image Fusion Based on NSST and NMF
  3.1. Fusion Frame
  3.2. The Fusion Rule of the Low Frequency Coefficients
  3.3. The Fusion Rule of the High Frequency Coefficients
 4. Experimental Results and Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Chen Zhen [ Information Engineering Department, Putian University, Putian, China ] Corresponding author
• Xing Xiaoxue [ College of Information Engineering, Changchun University, Changchun, China ]
• Guo Pan [ Institute of Science and Technology，College of Humanities & Sciences of Northeast Normal University, Changchun, China ]
• Fan Qinyin [ School of Engineering, Osaka University, Osaka, Japan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605