© The Institution of Engineering and Technology
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)
-
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. Smith, M.I., Heather, J.P.: ‘A review of image fusion technology in 2005’, Proc. SPIE, 2005, 5782, pp. 29–45.
-
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)
-
12. Ludusan, C., Lavialle, O.: ‘Multifocus image fusion and denoising: a variational approach’, Pattern Recognit. Lett., 2012, 33, pp. 1388–1396.
-
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)
-
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, . 9087, pp. 425–436.
-
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)
-
16. Lagerwerf, M.J.: ‘Higher order variational methods for photoacoustic tomography’. , University of Twenti, 2015.
-
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)
-
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, . 2, pp. 1260–1263.
-
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)
-
8. Piella, G.: ‘Image fusion for enhanced visualization: a variational approach’, Int. J. Comput. Vis., 2009, 83, pp. 1–11.
-
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)
-
7. Socolinsky, D.A., Wolff, L.B.: ‘Multispectral image visualization through first-order fusion’, IEEE Trans. Image Process., 2002, 11, pp. 923–931.
-
15)
-
1. Goshtasby, A.A., Nikolov, S.: ‘Image fusion: advances in the state of the art’, Inf. Fusion, 2007, 8, pp. 114–118.
-
16)
-
14. Bredies, K., Kunisch, K., Pock, T.: ‘Total generalized variation’, SIAM J. Imaging Sci., 2010, 3, pp. 492–526.
-
17)
-
4. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variational based noise removal algorithms’, Physica D, 1992, 60, pp. 259–268.
-
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)
-
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)
-
19. Pock, T., Zebedin, L., Bischof, H.: ‘TGV-fusion’, Lect. Notes Comput. Sci., 2011, 6570, pp. 245–258.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0451
Related content
content/journals/10.1049/iet-cvi.2017.0451
pub_keyword,iet_inspecKeyword,pub_concept
6
6