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Visible and infrared image fusion using ℓ0-generalized total variation model

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References

  1. Yun X, Jing Z L, Xiao G, et al. A compressive tracking based on time-space Kalman fusion model. Sci China Inf Sci, 2016, 59: 012106

    Article  Google Scholar 

  2. Toet A. Image fusion by a ratio of low-pass pyramid. Pattern Recogn Lett, 1989, 9: 245–253

    Article  MATH  Google Scholar 

  3. Da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process, 2006, 15: 3089–3101

    Article  Google Scholar 

  4. Nencini F, Garzelli A, Baronti S, et al. Remote sensing image fusion using the curvelet transform. Inf Fusion, 2007, 8: 143–156

    Article  Google Scholar 

  5. Ma J Y, Chen C, Li C, et al. Infrared and visible image fusion via gradient transfer and total variation minimization. Inf Fusion, 2016, 31: 100–109

    Article  Google Scholar 

  6. Jie Y, Lu W S. Compressive imaging by generalized total variation minimization. In: Proceedings of 2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Ishigaki, 2014. 21–24

    Google Scholar 

  7. Feng M B, Mitchell J E, Pang J S, et al. Complementarity formulations of l0 norm optimization problems. 2013. http://www.optimization-online.org/DB FILE/2013/09/4053.pdf

    Google Scholar 

  8. Yuan G Z, Ghanem B. A proximal alternating direction method for semi-definite rank minimization. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, 2016. 2300–2308

    Google Scholar 

  9. Xydeas C S, Petrovic V. Objective image fusion performance measure. Electron Lett, 2000, 36: 308–309

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Han Pan, Zhongliang Jing, Lingfeng Qiao & Minzhe Li

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  1. Han Pan
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  2. Zhongliang Jing
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  3. Lingfeng Qiao
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  4. Minzhe Li
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Correspondence to Han Pan or Zhongliang Jing.

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Pan, H., Jing, Z., Qiao, L. et al. Visible and infrared image fusion using ℓ0-generalized total variation model. Sci. China Inf. Sci. 61, 049103 (2018). https://doi.org/10.1007/s11432-017-9246-3

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  • DOI: https://doi.org/10.1007/s11432-017-9246-3

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