http://iet.metastore.ingenta.com
1887

Variational image fusion approach based on TGV and local information

Variational image fusion approach based on TGV and local information

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi-focus images as well as medical images of computed tomography and magnetic resonance. They use the second-order TGV as the regularisation term and local gradient information as the fusion weight to extract image features. To compute the new model effectively, the primal-dual algorithm is carried out. Various experiments are made to verify the effectiveness of the proposed methods.

References

    1. 1)
      • 1. Goshtasby, A.A., Nikolov, S.: ‘Image fusion: advances in the state of the art’, Inf. Fusion, 2007, 8, pp. 114118.
    2. 2)
      • 2. Smith, M.I., Heather, J.P.: ‘A review of image fusion technology in 2005’, Proc. SPIE, 2005, 5782, pp. 2945.
    3. 3)
      • 3. James, A.P., Belur, V.D.: ‘Medical image fusion: a survey of the state of the art’, Inf. Fusion, 2004, 19, pp. 419.
    4. 4)
      • 4. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variational based noise removal algorithms’, Physica D, 1992, 60, pp. 259268.
    5. 5)
      • 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. 6568.
    6. 6)
      • 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. 12601263.
    7. 7)
      • 7. Socolinsky, D.A., Wolff, L.B.: ‘Multispectral image visualization through first-order fusion’, IEEE Trans. Image Process., 2002, 11, pp. 923931.
    8. 8)
      • 8. Piella, G.: ‘Image fusion for enhanced visualization: a variational approach’, Int. J. Comput. Vis., 2009, 83, pp. 111.
    9. 9)
      • 9. Bertalmio, M., Levine, S.: ‘Variational approach for the fusion of exposure bracketed pairs’, IEEE Trans. Image Process., 2013, 22, pp. 712723.
    10. 10)
      • 10. Zhou, Z.Q., Li, S., Wang, B.: ‘Multi-scale weighted gradient-based fusion for multi-focus images’, Inf. Fusion, 2014, 20, pp. 6072.
    11. 11)
      • 11. Tang, S., Shen, C., Zhang, G.: ‘Adaptive regularized scheme for remote sensing image fusion’, Front. Earth Sci., 2016, 10, pp. 236244.
    12. 12)
      • 12. Ludusan, C., Lavialle, O.: ‘Multifocus image fusion and denoising: a variational approach’, Pattern Recognit. Lett., 2012, 33, pp. 13881396.
    13. 13)
      • 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. 425436.
    14. 14)
      • 14. Bredies, K., Kunisch, K., Pock, T.: ‘Total generalized variation’, SIAM J. Imaging Sci., 2010, 3, pp. 492526.
    15. 15)
      • 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.
    16. 16)
      • 16. Lagerwerf, M.J.: ‘Higher order variational methods for photoacoustic tomography’. MSc, University of Twenti, 2015.
    17. 17)
      • 17. Knoll, F., Bredies, K., Pock, T., et al: ‘Second order total generalized variation (TGV) for MRI’, Magn. Reson. Med., 2011, 65, pp. 480491.
    18. 18)
      • 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. 244248.
    19. 19)
      • 19. Pock, T., Zebedin, L., Bischof, H.: ‘TGV-fusion’, Lect. Notes Comput. Sci., 2011, 6570, pp. 245258.
    20. 20)
      • 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. 120145.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0451
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0451
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address