Multimedia Tools and Applications

, Volume 77, Issue 24, pp 32133–32151 | Cite as

Swarm intelligence based image fusion for noisy images using consecutive pixel intensity

  • Nirmala ParamanandhamEmail author
  • Kishore Rajendiran


A novel image fusion technique is presented, aiming at resolving the fusion problem of noisy images. In this paper, a new activity level measurement based on consecutive pixel intensity similarity is proposed for detecting the noise free and noisy parts from the source images and also the fusion technique is optimized using particle swarm optimization for obtaining the optimized fused image. Experiments have been made on images affected by Gaussian noise, salt and pepper impulsive noise, speckle noise and Poisson noises for examining the efficiency of the proposed algorithm. The proposed framework is evaluated using quantitative metrics such as root mean square error, peak signal to noise ratio, mean absolute error, percentage fit error, structural similarity index and mutual information. The experimental results demonstrate the outperformance of the proposed algorithm over many other well known state-of-the-art fusion techniques reported in the literature.


Block separation Consecutive pixel intensity similarity Noise Particle swarm optimization Tsallis entropy Mutual information 


  1. 1.
    Abdipour M, Nooshyar M (2016) Multi-focus image fusion using sharpness criteria for visual sensor networks in wavelet domain. Comput Electr Eng 51:74–88CrossRefGoogle Scholar
  2. 2.
    Boyat AK, Joshi BK (2015) A review paper: noise models in digital Image processing. Signal Image Proc: Int J (SIPIJ) 6(2)Google Scholar
  3. 3.
    Cao L, Jin L, Tao H, Li G, Zhuang Z, Zhang Y (2015) Multi-focus Image Fusion Based on Spatial Frequency in Discrete Cosine Transform Domain. IEEE Sign Proc Lett 22:(2)CrossRefGoogle Scholar
  4. 4.
    Cvejic, Canagarajah, Bull (2006) Image fusion metric based on mutual information and Tsallis entropy. Electron Lett 42(11)CrossRefGoogle Scholar
  5. 5.
    De Vore, Jawerth, Lucier (1992) Image compression through wavelet transform coding. IEEE Trans on Inf Theory 38(2):719–746MathSciNetCrossRefGoogle Scholar
  6. 6.
    Deepalaxmi, Balaji, Rajini (2013) Particle Swarm Optimization Based Selection of Optimal Polymeric Blend. IEEE Trans Dielect Electric Insul 20(3)CrossRefGoogle Scholar
  7. 7.
    Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, New York, pp 39–43Google Scholar
  8. 8.
    Guihong Q, Zhang D, Yan P (2002) Information measure for performance of image fusion. Electron Lett 38(7):313–315CrossRefGoogle Scholar
  9. 9. (accessed on 18/05/2018)
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    Indu CR (2009) Image fusion algorithm for impulse noise reduction. Inte Conf Adv Recent Technol Commun Comput, Kottayam, Kerala, India, 27–28 October 2009Google Scholar
  15. 15.
    Kausar N, Majid A, Javed SG (2016) A novel ensemble approach using individual features for multi-focus image fusion. Comput Electr Eng 54:393–405CrossRefGoogle Scholar
  16. 16.
    Khan SU, Khan NU, Chai WY (2016) Impulse Noise Reduction using Image Fusion Approach. The sixth international conference on innovative computing technology (INTECH)Google Scholar
  17. 17.
    Li X, Ni L (2015) Compressive Image Fusion Based on Particle Swarm Optimization Algorithm”, International Conference on Electronic Science and Automation Control (ESAC). Published by Atlantis PressGoogle Scholar
  18. 18.
    Li H, Chai Y, Ling R, Yin H (2013) Multifocus image fusion scheme using feature contrast of orientation information measure in lifting stationary wavelet domain. J Inf Sci Eng 29:227–247Google Scholar
  19. 19.
    Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Info Fusion 24:147–164CrossRefGoogle Scholar
  20. 20.
    Loza A, Bull D, Canagarajah N, Achim A (2010) Non-Gaussian model-based fusion of noisy images in the wavelet domain. Comput Vis Image Underst 114:54–65CrossRefGoogle Scholar
  21. 21.
    Naidu VPS, Raol JR (2008) Pixel-level image fusion using wavelets and principal component analysis. Defense Sci J 58(3):338–352CrossRefGoogle Scholar
  22. 22.
    Nejati M, Samavi S, Karimi N, Reza Soroushmehr SM, Shirani S, Roosta I, Najarian K (2017) Surface area-based focus criterion for multi-focus image fusion. Info Fusion 36:284–295CrossRefGoogle Scholar
  23. 23.
    Paramanandham N, Rajendiran K (2017) Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm. Multi Tools Appl 1–32Google Scholar
  24. 24.
    Paramanandham N, Rajendiran K (2018) Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications. Infrared Phys Technol 88:13–22CrossRefGoogle Scholar
  25. 25.
    Rahman M, Ahmad O, Swamy MNS (2010) Contrast-based fusion of noisy images using discrete wavelet transform. IET Image Process 4(5):374–384MathSciNetCrossRefGoogle Scholar
  26. 26.
    Raol JR (2010) Multi-sensor data fusion with Matlab. CRC Press, Taylor and Francis GroupGoogle Scholar
  27. 27.
    Srivastava K (2015) Multifocus noisy image fusion using contourlet transform. Imaging Sci J 63(7):408–422CrossRefGoogle Scholar
  28. 28.
    Srivastava, Singh, Khare (2016) Fusion of multifocus noisy images using contourlet transform. Contemporary Computing (IC3), 2013 Sixth International Conference on, Noida 497–502Google Scholar
  29. 29.
    Toet A, Hogervorst MA, Nikolov SG, Lewis JJ, Dixon TD, Bull DR, Canagarajah CN (2010) Towards cognitive image fusion. Info Fusion 11:95–113CrossRefGoogle Scholar
  30. 30.
    Wang Z, Ziou D, Armenakis C, Li D, Li Q (2005) A Comparative Analysis of Image Fusion Methods. IEEE Trans Geosci Remote Sensing 43: (6)Google Scholar
  31. 31.
    Yang Y, Tong S, Huang S, Lin P (2015) Multifocus Image Fusion Based on NSCT and Focused Area Detection. IEEE Sensor JGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSSN college of EngineeringChennaiIndia

Personalised recommendations