[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A novel intuitionistic-near fuzzy sets based image fusion approach: development on hybrid MPI+OpenMP parallel model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image fusion is used to extract relevant features from different image modalities like infrared and visual images and to combine them into a single image effectively. In this article, we have introduced a hybrid-parallel intuitionistic-near set based fusion scheme through near feature map approach. The proposed hybrid-parallel fusion scheme fully utilizes distributed memory parallelism and OpenMP for shared-memory parallelism. First, the fuzzy image representation based intuitionistic fuzzy theory is considered. Second, the principal features in the infrared and visual images are mapped using near-fuzzy set. Third, final fusion features are measured from decomposed multiple image blocks through domain decomposition strategy and image features are extracted via a defined probe function. After that, the near features have been computed from both the original images via intuitionistic entropy-based probe function, the ultimate fusion image is achieved through perceptual threshold limit on the membership grades in the fuzzy space. Finally, the resultant fused image is obtained through defuzzification. A hybrid MPI and OpenMP model is adopted to reduce inter-node communication and parallelized codes. The experimental result shows that the proposed method effectively combines the relevant information of both source images and provides a high resolution image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Listing 1
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Aggarwal J (1993) Multisensor Fusion for Computer Vision

  2. Atanassov K (1999) Intuitionistic fuzzy sets: theory and applications. Studies in fuzziness and soft computing. Physicaverl, New York, p 1999

    Book  Google Scholar 

  3. Balasubramaniam P, Ananthi V (2014) Image fusion using intuitionistic fuzzy sets. Inform Fus 20:21–30

    Article  Google Scholar 

  4. Barney B (2010) Introduction to parallel computing. https://computing.llnl.gov/tutorials/parallel-comp

  5. Bova S, Breshears C, et al. (2001) Parallel programming with message passing and directives. Comput Sci Eng 3(4):22–37

    Article  Google Scholar 

  6. Burillo P, Bustince H (1996) Entropy on intuitionistic fuzzy sets and on intervalued. Tech Rep, 3

  7. Candes E, Demanet L, et al. (2006) Fast discrete curvelet transforms. SIAM Multiscale Model Simul 5(3):861–899

    Article  MathSciNet  Google Scholar 

  8. Chaira T (2011) A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl Soft Comput 11:1711–1717

    Article  Google Scholar 

  9. Chaira T (2012) A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set. Appl Soft Comput 12(4):1259–1266

    Article  Google Scholar 

  10. Chao Z, Kim D, Kim HJ (2018) Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks. Phys Med 48:11–20

    Article  Google Scholar 

  11. Cunha L, Zhou J (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101

    Article  Google Scholar 

  12. David T (2000) C++ Template image processing toolkit. http://cimg.eu

  13. Dubois D, Prade H (1980) Fuzzy sets and systems, theory and applications. NY Academic Press, New York

    MATH  Google Scholar 

  14. Goshtasby A, Nikolov S (2007) Image fusion: advances in the state of the art. Inf Fusion 8(2):114–118

    Article  Google Scholar 

  15. Gropp W, Lusk E, Thakur R (1999) Using MPI-2: advanced features of the message-passing interface. MIT Press

  16. Hall D, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23

    Article  Google Scholar 

  17. Jiang Q, Jin X, et al. (2018) Multi-sensor image fusion based on interval type-2 fuzzy sets and regional features in nonsubsampled shearlet transform domain. IEEE Sensors J 18:2494–2505

    Article  Google Scholar 

  18. Jiayi M, Yong M, L C (2019) Infrared and visible image fusion methods and applications: a survey. Inform Fusion 45:153–178

    Article  Google Scholar 

  19. Kirk D, Hwu W (2010) Programming massively parallel processors: a hands-on approach. Morgan Kaufmann Publishers Inc, San Francisco, pp 900–909

    Google Scholar 

  20. Kong W, Wang B, Lei Y (2015) Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model. Infrared Phys Technol 71:87–98

    Article  Google Scholar 

  21. Li H, Manjunath B, Mitra S (1995) Multisensor image fusion using the wavelet transform. Graph Model Image Process 57(3):235–245

    Article  Google Scholar 

  22. Manchanda M, Sharma R (2018) An improved multimodal medical image fusion algorithm based on fuzzy transform. J Vis Commun Image Represent 51:76–94

    Article  Google Scholar 

  23. MPICH (2016) Message Passing Interface (MPI)

  24. Pal S, King R (1989) Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst Man Cybern 11(7):494–501

    Google Scholar 

  25. Peters J, Piotr W (2008) Foundations of near sets. Inform Sci 179:3091–3109

    Article  MathSciNet  Google Scholar 

  26. Quinn M (2003) Parallel programming in C with MPI and OpenMP. McGraw Hill, New York

    Google Scholar 

  27. Rahman M, Liu S, Wong Y (2017) Multi-focal image fusion using degree of focus and fuzzy logic. Digit Signal Process 60:1–19

    Article  Google Scholar 

  28. RGB-NIR Image and Visual Representation Lab (IVRL). https://ivrl.epfl.ch

  29. Shutao L, Xudong K, Leyuan F (2017) Pixel-level image fusion: a survey of the state of the art. Inform Fusion 33:100–112

    Article  Google Scholar 

  30. Siegel L, Siegel H, Feather A (1982) Parallel processing approaches to image correlation. IEEE Trans Comput C-31(3):208–218

    Article  Google Scholar 

  31. Szmidt E, Kacpryzyk J (2000) Distance between intuitionistic fuzzy set. Fuzzy Sets Syst 114(3):505–518

    Article  MathSciNet  Google Scholar 

  32. Tuncer I, Glcat U et al (2007) Parallel computational fluid dynamics. LNCSE 67:401–408

    Google Scholar 

  33. Yager R (1979) On the measure of fuzziness and negation. Part II: lattices, and Inf. Control 44(3):236–260

    Article  Google Scholar 

  34. Yang Y, Que Y, et al. (2016) Multimodal sensor medical image fusion based on type-2 fuzzy logic in NSCTDomain. IEEE Sensors J 16(10):3735–3745

    Article  Google Scholar 

  35. Zhi X, FAN J (2008) Generalized fuzzy complement and corresponding generalized fuzzy entropy. Fuzzy Syst Math 22(1):96–102

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Biswajit Biswas

  2. Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India

    Swarup Kr Ghosh

  3. Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India

    Anupam Ghosh

Authors
  1. Biswajit Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swarup Kr Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anupam Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swarup Kr Ghosh.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Biswas, B., Ghosh, S.K. & Ghosh, A. A novel intuitionistic-near fuzzy sets based image fusion approach: development on hybrid MPI+OpenMP parallel model. Multimed Tools Appl 81, 29699–29730 (2022). https://doi.org/10.1007/s11042-022-12333-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12333-0

Keywords

Search

Navigation