OMP or BP? A Comparison Study of Image Fusion Based on Joint Sparse Representation

  • Yao Yao
  • Xin Xin
  • Ping Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7667)


In image fusion techniques based on joint sparse representation (JSR), the composite image is calculated from the fusion of features, which are represented with sparse coefficients. Orthogonal matching pursuit (OMP) and basis pursuit (BP) are the main candidates to estimate the coefficients. Previously OMP is utilized for the advantage of low complexity. However, noticeable errors occur when the dictionary of JSR cannot ensure the coefficients are sparse enough. Alternatively, BP is more robust than OMP in such cases (though suffered from larger complexity). Unfortunately, it has never been studied in image fusion tasks. In this paper, we investigate JSR based on BP for image fusion. The target is to verify that 1) to what extent can BP outperform OMP; and 2) what is the trade-off between BP and OMP. Finally, we conclude, in some cases, fusion with BP obviously outperforms the one with OMP under an affordable computational complexity.


Joint sparse representation Image fusion Sparse coefficient approximation Basis pursuit Orthogonal matching pursuit 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yao Yao
    • 1
  • Xin Xin
    • 1
  • Ping Guo
    • 1
  1. 1.School of Computer Science & TechnologyBeijing Institute of TechnologyBeijingChina

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