Circuits, Systems, and Signal Processing

, Volume 38, Issue 12, pp 5576–5605 | Cite as

Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach

  • Durga Prasad Bavirisetti
  • Gang XiaoEmail author
  • Junhao Zhao
  • Ravindra Dhuli
  • Gang Liu


In this paper, we propose a general purpose, simple and fast fusion algorithm based on guided image filter. The proposed method can well combine useful source image information into the fused image supported by multi-scale image decomposition, structure transferring property, visual saliency detection, and weight map construction. Multi-scale image decomposition is appropriate to represent and manipulate image features at various scales. Structure transferring property enabled by our algorithm can induce structures of one source image into the other. A new visual saliency detection based on guided image filter introduced in this paper is able to extract significant regions from visually different images of the same scene. The choice of weight maps helped to integrate the complementary information pixel by pixel at each scale. Experimental outcomes of the proposed method are compared and analyzed with traditional and recent guided image filter-based fusion algorithms in terms of visual quality, fusion metrics and run time. In addition, to enhance fusion results further we made an effort to find a suitable image and video enhancement algorithm. The fusion performance analysis clearly indicates that the proposed method is very promising along with less run time.


Edge preserving Guided image filter Image fusion Multi-scale image decomposition Structure transferring Visual saliency 



This work is sponsored by National Program on Key Basic Research Project (2014CB744903), National Natural Science Foundation of China (61673270), Shanghai Pujiang Program(16PJD028), Shanghai Industrial Strengthening Project (GYQJ-2017-5-08), Shanghai Science and Technology Committee Research Project (17DZ1204304) and Shanghai Engineering Research Center of Civil Aircraft Flight Testing. We would like to thank our postdoctoral researcher Dr. Xingchen Zhang for English proof reading of the manuscript.


  1. 1.
    D.P. Bavirisetti, R. Dhuli, Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sens. J. 16(1), 203–209 (2016)CrossRefGoogle Scholar
  2. 2.
    D.P. Bavirisetti, R. Dhuli, Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phys. Technol. 76, 52–64 (2016)CrossRefGoogle Scholar
  3. 3.
    D.P. Bavirisetti, R. Dhuli, Multi-filtering based edge preserving image fusion technique. Int. J. Serv. Technol. Manage 23(4), 275–289 (2017)CrossRefGoogle Scholar
  4. 4.
    D.P. Bavirisetti, V.K. Kollu, X. Gang, R. Dhuli, Fusion of MRI and CT images using guided image filter and image statistics. Int. J. Imaging Syst. Technol. 27, 227–237 (2017)CrossRefGoogle Scholar
  5. 5.
    D.P. Bavirisetti et al., A new image and video fusion method based on cross bilateral filter, in 2018 21st International Conference on Information Fusion (FUSION). IEEE (2018)Google Scholar
  6. 6.
    G. Bhatnagar, Q.M.J. Wu, Z. Liu, Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Trans. Multimed. 15(5), 1014–1024 (2013)CrossRefGoogle Scholar
  7. 7.
    R.S. Blum, Z. Liu (eds.), Multi-sensor image fusion and its applications (CRC Press, Boca Raton, 2005)Google Scholar
  8. 8.
    S. Chen, A. Ramli, Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49(4), 1301–1309 (2003)CrossRefGoogle Scholar
  9. 9.
    J.-C. Chiang et al., High-dynamic-range image generation and coding for multi-exposure multi-view images. Circuits Syst. Signal Process. 36(7), 2786–2814 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    R.R. Colditz et al., Influence of image fusion approaches on classification accuracy: a case study. Int. J. Remote Sens. 27(15), 3311–3335 (2006)CrossRefGoogle Scholar
  11. 11.
    N.D. Duong, S.D. Tio, A.S. Madhukumar, A cooperative spectrum sensing technique with dynamic frequency boundary detection and information-entropy-fusion for primary user detection. Circuits Syst. Signal Process. 30(4), 823–845 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    W. Gan et al., Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys. Technol. 72, 37–51 (2015)CrossRefGoogle Scholar
  13. 13.
    R.C. Gonzalez, R.E. Woods, in The Book, Digital Image Processing [M]. Publishing house of electronics industry 141.7 (2002)Google Scholar
  14. 14.
    Y. Han et al., A new image fusion performance metric based on visual information fidelity. Inform. Fusion 14(2), 127–135 (2013)CrossRefGoogle Scholar
  15. 15.
    K. He, J. Sun, X. Tang, Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  16. 16.
    L. Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  17. 17.
    A. Jameel, A. Ghafoor, M.M. Riaz, Improved guided image fusion for magnetic resonance and computed tomography imaging. Sci. World J. (2014). CrossRefGoogle Scholar
  18. 18.
    A. Jameel, A. Ghafoor, M.M. Riaz, Wavelet and guided filter based multifocus fusion for noisy images. Optik Int. J. Light Electron Opt. 126(23), 3920–3923 (2015)CrossRefGoogle Scholar
  19. 19.
    A. Jameel, A. Ghafoor, M.M. Riaz, All in focus fusion using guided filter. Multidimension. Syst. Signal Process. 26(3), 879–889 (2015)CrossRefGoogle Scholar
  20. 20.
    U. Javed et al., Weighted fusion of MRI and PET images based on fractal dimension. Multidimension. Syst. Signal Process. 28(2), 679–690 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    F.T. Jhohura, T. Howlader, S.M. Rahman, Bayesian fusion of ensemble of multifocused noisy images. Circuits Syst. Signal Process. 34(7), 2287–2308 (2015)CrossRefGoogle Scholar
  22. 22.
    Y. Kim, Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (2002)CrossRefGoogle Scholar
  23. 23.
    M. Kim, M. Chung, Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans. Consum. Electron. 54(3), 1389–1397 (2008)CrossRefGoogle Scholar
  24. 24.
    S. Li, X. Kang, H. Jianwen, Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)CrossRefGoogle Scholar
  25. 25.
    J. Li et al., Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013)CrossRefGoogle Scholar
  26. 26.
    S. Liu et al., Image fusion based on complex-shearlet domain with guided filtering. Multidimension. Syst. Signal Process. 28(1), 207–224 (2017)CrossRefGoogle Scholar
  27. 27.
    X. Ma et al, Saliency analysis based on multi-scale wavelet decomposition, in 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC). IEEE (2013)Google Scholar
  28. 28.
    K. Murahira, T. Kawakami, A. Taguchi, Modified histogram equalization for image contrast enhancement, in 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, pp. 1–5 (2010)Google Scholar
  29. 29.
    S. Pachori, S. Raman, Multi-scale Saliency Detection using Dictionary Learning. arXiv preprint arXiv:1611.06307 (2016)
  30. 30.
    M. Peng et al., Fault diagnosis of analog circuits using systematic tests based on data fusion. Circuits Syst. Signal Process. 32(2), 525–539 (2013)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Pritika, S. Budhiraja, Multimodal medical image fusion based on guided filtered multi-scale decomposition. Int. J. Biomed. Eng. Technol. 20(4), 285–301 (2016)CrossRefGoogle Scholar
  32. 32.
    S. Singh et al., Infrared and visible image fusion for face recognition, in Proceedings of SPIE, vol. 5404 (2004)Google Scholar
  33. 33.
    H. Singh, V. Kumar, S. Bhooshan, A novel approach for detail-enhanced exposure fusion using guided filter. Sci. World J. (2014).
  34. 34.
    P. Shah, S.N. Merchant, U.B. Desai, Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. Signal Image Video Process. 7(1), 95–109 (2013). CrossRefGoogle Scholar
  35. 35.
    P. Shah et al., Multimodal image/video fusion rule using generalized pixel significance based on statistical properties of the neighborhood. Signal Image Video Process. 8(4), 723–738 (2014)CrossRefGoogle Scholar
  36. 36.
    L. Shuaiqi, Z. Jie, S. Mingzhu, Medical image fusion based on rolling guidance filter and spiking cortical model. Comput. Math. Methods Med. (2015).
  37. 37.
    A. Toet, Iterative guided image fusion. Peer J. Comput. Sci. 2, e80 (2016)CrossRefGoogle Scholar
  38. 38.
    A. Toet, M.A. Hogervorst, Multiscale image fusion through guided filtering. SPIE Security + Defence. Int. Soc. Opt. Photonics (2016)Google Scholar
  39. 39.
    N. Xu et al., Object tracking via deep multi-view compressive model for visible and infrared sequences, in 2018 21st International Conference on Information Fusion (FUSION). IEEE (2018)Google Scholar
  40. 40.
    N. Xu et al., Relative object tracking algorithm based on convolutional neural network for visible and infrared video sequences, in Proceedings of the 4th International Conference on Virtual Reality. ACM (2018)Google Scholar
  41. 41.
    C. Zhang, A.A. Sufi, Color enhancement in image fusion, in IEEE Workshop on Applications of Computer Vision, 2008. WACV 2008. IEEE (2008)Google Scholar
  42. 42.
    T. Zhang et al., A novel method of signal fusion based on dimension expansion. Circuits Syst. Signal Process. 37(10), 4295–4318 (2018). MathSciNetCrossRefGoogle Scholar
  43. 43.
    W. Zhou, A.C. Bovik, A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  44. 44.
    Z. Zhou et al., Fusion of infrared and visible images for night-vision context enhancement. Appl. Opt. 55(23), 6480–6490 (2016)CrossRefGoogle Scholar
  45. 45.
    K. Zuiderveld, Contrast limited adaptive histogram equalization. Graphic gems IV (Academic Press Professional, San Diego, 1994), pp. 474–485Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Durga Prasad Bavirisetti
    • 1
  • Gang Xiao
    • 1
    Email author
  • Junhao Zhao
    • 1
  • Ravindra Dhuli
    • 2
  • Gang Liu
    • 3
  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Electronics EngineeringVIT UniversityAmaravatiIndia
  3. 3.School of Automation EngineeringShanghai University of Electrical PowerShanghaiChina

Personalised recommendations