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

Robust image fusion with block sparse representation and online dictionary learning

Robust image fusion with block sparse representation and online dictionary learning

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

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(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 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 Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

For many image fusion problems, the most used technique is selecting features with rich information. The robust image fusion method based on block compressive sensing principle is studied here. Compressive sensing is known to provide an effective method with high accuracy. The framework of the proposed method is given in various perspectives: block sparse representations, restoration algorithms, feature extraction, online dictionary learning, and fusion rules. In terms of restoration of fused images, the split Bregman iteration is adopted. The proposed method can acquire well fusion image from source images and remove some degradations simultaneously, such as noises and blurring effect. In addition, both ‘maximum selection’ and ‘weighted mean’ are investigated as fusion rules, which can preserve more information. Generally, the proposed method can achieve better fusion result from the source images. The experiments with or without noise source images both illustrate that the proposed method has relatively comparative fusion results.

References

    1. 1)
      • X. Zhou , W. Wang , R.-a. Liu .
        1. Zhou, X., Wang, W., Liu, R.-a.: ‘Compressive sensing image fusion algorithm based on directionlets’, EURASIP J. Wirel. Commun. Netw., 2014, 1, pp. 16.
        . EURASIP J. Wirel. Commun. Netw. , 1 - 6
    2. 2)
      • N. Yu , T. Qiu , F. Bi .
        2. Yu, N., Qiu, T., Bi, F., et al: ‘Image features extraction and fusion based on joint sparse representation’, IEEE J. Sel. Topics Signal Process., 2011, 5, pp. 10741082.
        . IEEE J. Sel. Topics Signal Process. , 1074 - 1082
    3. 3)
      • A. Divekar , O. Ersoy .
        3. Divekar, A., Ersoy, O.: ‘Theory and applications of compressive sensing’. ECE Technical Reports, 2010, pp. 177.
        . , 1 - 77
    4. 4)
      • Q. Yang , H. J. Wang , X. Luo .
        4. Yang, Q., Wang, H. J., Luo, X.: ‘Research on remote sensing image fusion algorithm based on compressed sensing’, Int. J. Hybrid Inf. Technol., 2015, 8, pp. 283292.
        . Int. J. Hybrid Inf. Technol. , 283 - 292
    5. 5)
      • T. Wan , N. Canagarajah , A. Achim .
        5. Wan, T., Canagarajah, N., Achim, A.: ‘Compressive image fusion’. Proc. of the Int. Conf. on Image Processing, California, USA, 2008, pp. 13081311.
        . Proc. of the Int. Conf. on Image Processing , 1308 - 1311
    6. 6)
      • X. Luo , J. Zhang , J. Yang .
        6. Luo, X., Zhang, J., Yang, J., et al: ‘Image fusion in compressed sensing’. Proc. of the Int. Conf. on Image Processing, 2009, pp. 22052208.
        . Proc. of the Int. Conf. on Image Processing , 2205 - 2208
    7. 7)
      • Y. Shen , J. Dang , Y. Wang .
        7. Shen, Y., Dang, J., Wang, Y., et al: ‘Infrared and visible light image fusion algorithm based on compressed sensing’, J. Inf. Comput. Sci., 2015, 12, pp. 13371347.
        . J. Inf. Comput. Sci. , 1337 - 1347
    8. 8)
      • O. Rockinger , T. Fechner .
        8. Rockinger, O., Fechner, T.: ‘Pixel-level image fusion: the case of image sequences’. Proc. SPIE, Bellingham, WA, 1998, pp. 378388.
        . Proc. SPIE , 378 - 388
    9. 9)
      • A. Toet , J. Walraven .
        9. Toet, A., Walraven, J.: ‘New false color mapping for image fusion’, Opt. Eng.., 1996, 35, pp. 650658.
        . Opt. Eng.. , 650 - 658
    10. 10)
      • A.M. Waxman , D.A. Fay , A.N. Gove .
        10. Waxman, A.M., Fay, D.A., Gove, A.N.: ‘Color night vision: fusion of intensified visible and thermal IR imagery’. Proc. of SPIE Synthetic Vision for Vehicle Guidance and Control, 1995, pp. 5868.
        . Proc. of SPIE Synthetic Vision for Vehicle Guidance and Control , 58 - 68
    11. 11)
      • R.K. Sharma , T.K. Leen , M. Pavel .
        11. Sharma, R.K., Leen, T.K., Pavel, M.: ‘Bayesian sensor image fusion using local linear generative models’, Opt. Eng., 2001, 40, pp. 13641376.
        . Opt. Eng. , 1364 - 1376
    12. 12)
      • J.M. Lafert , F. Heitz , P. Perez .
        12. Lafert, J.M., Heitz, F., Perez, P.: ‘Hierarchical statistical models for the fusion of multiresolution image data’. ICCV, Cambridge, USA, 1995, pp. 908913.
        . ICCV , 908 - 913
    13. 13)
      • P.J. Burt , E.H. Adelson .
        13. Burt, P.J., Adelson, E.H.: ‘The Laplacian pyramid as a compact image code’, IEEE Trans. Commun., 1983, 31, pp. 532540.
        . IEEE Trans. Commun. , 532 - 540
    14. 14)
      • H. Li , B.S. Manjunath , S.K. Mitra .
        14. Li, H., Manjunath, B.S., Mitra, S.K.: ‘Multisensor image fusion using the wavelet transform’, Graph. Models Image Process., 1995, 57, pp. 235245.
        . Graph. Models Image Process. , 235 - 245
    15. 15)
      • X. Qu , J. Yan , G Xie .
        15. Qu, X., Yan, J., Xie, G, et al: ‘A novel image fusion algorithm based on bandelet transform’, Chin. Opt. Lett., 2007, 5, pp. 569572.
        . Chin. Opt. Lett. , 569 - 572
    16. 16)
      • X.-b. Qu , J.-w. Yan , G.-d. Yang .
        16. Qu, X.-b., Yan, J.-w., Yang, G.-d.: ‘Sum-modified-Laplacian-based multifocus image fusion method in sharp frequency localized contourlet transform domain’, Opt. Precis. Eng., 2009, 17, pp. 12031212.
        . Opt. Precis. Eng. , 1203 - 1212
    17. 17)
      • N. Mitianoudis , T. Stathaki .
        17. Mitianoudis, N., Stathaki, T.: ‘Pixel-based and region-based image fusion schemes using ICA bases’, Inf. Fusion., 2007, 8, pp. 131142.
        . Inf. Fusion. , 131 - 142
    18. 18)
      • E.T. Hale , W. Yin , Y. Zhang .
        18. Hale, E.T., Yin, W., Zhang, Y.: ‘A fixed-point continuation method for l1-regularized minimization with applications to compressed sensing’. CAAM Technical Report TR07-07, 2007.
        .
    19. 19)
      • T. Goldstein , S. Osher .
        19. Goldstein, T., Osher, S.: ‘The split Bregman method for L1 regularized problems’, SIAM J. Imaging Sci., 2009, 2, pp. 323343.
        . SIAM J. Imaging Sci. , 323 - 343
    20. 20)
      • B. Yang , S. Li .
        20. Yang, B., Li, S.: ‘Multifocus image fusion and restoration with sparse representation’, IEEE Trans. Instrum. Meas., 2010, 59, pp. 884891.
        . IEEE Trans. Instrum. Meas. , 884 - 891
    21. 21)
      • J. Mairal , F. Bach , J. Ponce .
        21. Mairal, J., Bach, F., Ponce, J., et al: ‘Online dictionary learning for sparse coding’, Proc. of the 26 th Int. Conf. on Machine Learning, Montreal, Canada, 2009, pp. 689696.
        . Proc. of the 26 th Int. Conf. on Machine Learning , 689 - 696
    22. 22)
      • V. Naidu , B. Elias .
        22. Naidu, V., Elias, B.: ‘A novel image fusion technique using DCT based Laplacian pyramid’, Int. J. Inventive Eng. Sci., 2013, 1, pp. 19.
        . Int. J. Inventive Eng. Sci. , 1 - 9
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0327
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0327
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address