References

• 1. 1)
     • 9. Bertalmio, M., Levine, S.: ‘Variational approach for the fusion of exposure bracketed pairs’, IEEE Trans. Image Process., 2013, 22, pp. 712–723.
  2. 2)
     • 2. Smith, M.I., Heather, J.P.: ‘A review of image fusion technology in 2005’, Proc. SPIE, 2005, 5782, pp. 29–45.
  3. 3)
     • 17. Knoll, F., Bredies, K., Pock, T., et al: ‘Second order total generalized variation (TGV) for MRI’, Magn. Reson. Med., 2011, 65, pp. 480–491.
  4. 4)
     • 12. Ludusan, C., Lavialle, O.: ‘Multifocus image fusion and denoising: a variational approach’, Pattern Recognit. Lett., 2012, 33, pp. 1388–1396.
  5. 5)
     • 11. Tang, S., Shen, C., Zhang, G.: ‘Adaptive regularized scheme for remote sensing image fusion’, Front. Earth Sci., 2016, 10, pp. 236–244.
  6. 6)
     • 13. Hafner, D., Weickert, J.: ‘Variational exposure fusion with optimal local contrast’. Proc. Int. Conf. Scale Space and Variational Methods in Computer Vision, Cham, 2015, vol. 9087, pp. 425–436.
  7. 7)
     • 5. Wang, W.W., Shui, P.L., Feng, X.C.: ‘Variational models for fusion and denoising of multifocus images’, IEEE Signal Process. Lett., 2008, 15, pp. 65–68.
  8. 8)
     • 16. Lagerwerf, M.J.: ‘Higher order variational methods for photoacoustic tomography’. MSc, University of Twenti, 2015.
  9. 9)
     • 10. Zhou, Z.Q., Li, S., Wang, B.: ‘Multi-scale weighted gradient-based fusion for multi-focus images’, Inf. Fusion, 2014, 20, pp. 60–72.
  10. 10)
      • 6. Sarode, M.V., Deshmukh, P.R.: ‘Evaluation of fusion and denoising algorithm for multifocus images’. Proc. Int. Conf. World Congress on Engineering, London, UK, July 2011, vol. 2, pp. 1260–1263.
  11. 11)
      • 18. Wang, S., Guo, W., Huang, T.Z.: ‘Weighted total generalized variation for compressive sensing reconstruction’. Proc. 11th Int. Conf. Sampling Theory and Applications, Washington, DC, USA, May 2015, pp. 244–248.
  12. 12)
      • 8. Piella, G.: ‘Image fusion for enhanced visualization: a variational approach’, Int. J. Comput. Vis., 2009, 83, pp. 1–11.
  13. 13)
      • 3. James, A.P., Belur, V.D.: ‘Medical image fusion: a survey of the state of the art’, Inf. Fusion, 2004, 19, pp. 4–19.
  14. 14)
      • 7. Socolinsky, D.A., Wolff, L.B.: ‘Multispectral image visualization through first-order fusion’, IEEE Trans. Image Process., 2002, 11, pp. 923–931.
  15. 15)
      • 1. Goshtasby, A.A., Nikolov, S.: ‘Image fusion: advances in the state of the art’, Inf. Fusion, 2007, 8, pp. 114–118.
  16. 16)
      • 14. Bredies, K., Kunisch, K., Pock, T.: ‘Total generalized variation’, SIAM J. Imaging Sci., 2010, 3, pp. 492–526.
  17. 17)
      • 4. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variational based noise removal algorithms’, Physica D, 1992, 60, pp. 259–268.
  18. 18)
      • 20. Chambolle, A., Pock, T.: ‘A first-order primal-dual algorithm for convex problems with applications to imaging’, J. Math. Imaging Vis., 2011, 40, pp. 120–145.
  19. 19)
      • 15. Lu, H., Wei, J., Liu, Q., et al: ‘A dictionary learning method with total generalized variation for MRI reconstruction’, Int. J. Biomed. Imaging, 2016, 2016, pp. 751.
  20. 20)
      • 19. Pock, T., Zebedin, L., Bischof, H.: ‘TGV-fusion’, Lect. Notes Comput. Sci., 2011, 6570, pp. 245–258.