Advertisement

Multi-focus Image Fusion: Quantitative and Qualitative Comparative Analysis

  • Shiveta Bhat
  • Deepika KoundalEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)

Abstract

Multi-focus Image Fusion (MFIF) is a technique that combines multiple images to obtain a composite image in which all the objects are in-focus and have improved image quality. More information is stored by the focused image than that of the information stored by the source image. MFIF provides fused images which can be used for various image processing tasks like target recognition, feature extraction, and segmentation. There exists number of MFIF techniques in spatial as well as transform domain such as Stationary Wavelet Transform, Discrete Wavelet Transform, and Principal Component Analysis. In this paper, comparative analysis of various MFIF techniques which are used to fuse multi-focused images is done. Qualitative as well as quantitative evaluation has been carried out for various MFIF techniques. MFIF provides a fused image which helps for high resolution of vision. Various challenges/issues related to the existing MFIF techniques are also highlighted and will be helpful in the future.

Keywords

Multi-focus images SWT DWT and PCA 

References

  1. 1.
    Liu, Y., Liu, S., Wang, Z.: Multi-focus image fusion with dense SIFT. Inf. Fusion 23, 139–155 (2015)CrossRefGoogle Scholar
  2. 2.
    Tang, H., Xiao, B., Li, W., Wang, G.: Pixel convolution neural network for multi-focus image fusion. Inf. Sci. 433–434, 125–141 (2018)CrossRefGoogle Scholar
  3. 3.
    Kaur, G., Kaur, P.: Survey on multifocus image fusion techniques. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016Google Scholar
  4. 4.
    Virk, S.R.: Review of image fusion techniques. Int. Res. J. Eng. Technol. (IRJET) 2 (2015)Google Scholar
  5. 5.
    Sharma, M.: A review: image fusion techniques and applications. Int. J. Comput. Sci. Inf. Technol. 7 (2016)Google Scholar
  6. 6.
    Wang, Z., Ma, Y.: Medical image fusion using m-PCNN. Inf. Fusion 9, 176–185 (2008)CrossRefGoogle Scholar
  7. 7.
    Jiang, Z.G., Han, D.B., Chen. J., Zhou, X.K., A wavelet based algorithm for multi-focus micro-image fusion. In: Proceedings of the Third International Conference on Image and Graphics (ICIG) (2004), pp. 176–179Google Scholar
  8. 8.
    Sujatha, K., Punithavathani, D.S.: Optimized ensemble decision-based multi-focus image fusion using binary genetic Grey-Wolf optimizer in camera sensor networks. Multimed. Tools Appl. 77, 1735–1759 (2018)CrossRefGoogle Scholar
  9. 9.
    Simone, G., Farina, A., Morabito, F.C., Serpico, S.B., Bruzzone, L.: Image fusion techniques for remote sensing applications. Inf. fusion 3, 3–15 (2002)CrossRefGoogle Scholar
  10. 10.
    Chen, Z, Wang, D, Gong, S., Zhao, F.: Application of multi-focus image fusion in visual power patrol inspection. In: 2nd Advanced Information Technology Electronic and Automation Control Conference (IAEAC) (2017), pp. 1688–1692Google Scholar
  11. 11.
    Song, Y., Li, M., Li, Q., Sun, L., A new wavelet based multi-focus image fusion scheme and its application on optical microscopy. In: International Conference on Robotics and Biomimetics (ROBIO) (2006) pp. 401–405Google Scholar
  12. 12.
    Plas, R.V.D., Yang, J., Spraggins, J., Caprioli, R.M.: Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping. Nat. Methods 12, 366–372 (2015)CrossRefGoogle Scholar
  13. 13.
    Yang, Y., Zheng, W., Huang, S.: Effective multifocus image fusion based on HVS and BP neural network. The Sci. World J. 2014, 1–10 (2014)Google Scholar
  14. 14.
    Kaur, H., Rani, E.J.: Analytical comparison of various image fusion techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5 (2015)Google Scholar
  15. 15.
    Siddiqui, A.B., Rashid, M., Jaffar, M.A., Hussain, A., Mirza, A.M., Feature classification for multi-focus image fusion. Int. J. Phys. Sci. 6, 4838–4847 (2011)Google Scholar
  16. 16.
    Garg, R., Gupta, P., Kaur, H.: Survey on multi-focus image fusion algorithms. In: Recent Advances in Engineering and Computational Sciences (RAECS) (2014), pp. 1–5Google Scholar
  17. 17.
    Nejati, M., Samavi, S., Karimi, N., Soroushmehr, S.R., Shirani, S., Roosta, I., Najarian, K.: Surface area-based focus criterion for multi-focus image fusion. Inf. Fusion 36, 284–295 (2017)CrossRefGoogle Scholar
  18. 18.
    Kannan, K., Perumal, A.S., Arulmozhi, K.: Optimal decomposition level of discrete, stationary and dual tree complex wavelet transform for pixel based fusion of multi-focused images. Serbian J. Electr. Eng. 7, 81–93 (2010)CrossRefGoogle Scholar
  19. 19.
    Sahu, D.K., Parsai, M.P.: Different image fusion techniques–a critical review. Int. J. Mod. Eng. Res. (IJMER) 2, 4298–4301 (2012)Google Scholar
  20. 20.
    Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: Multi-focus image fusion for visual sensor networks in DCT domain. Comput. Electr. Eng. 37, 789–797 (2011)CrossRefGoogle Scholar
  21. 21.
    Wang, Z., Ma, Y., Gu, J.: Multi-focus image fusion using PCNN. Pattern Recogn. 43, 2003–2016 (2010)CrossRefGoogle Scholar
  22. 22.
    Pajares, G., Cruz, J.M.: A wavelet-based image fusion tutorial. Pattern Recogn. 37, 1855–1872 (2004)CrossRefGoogle Scholar
  23. 23.
    Miao, Q., Wang, B.: A novel adaptive multi-focus image fusion algorithm based on PCNN and sharpness. In: Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense (2005), pp. 704–713Google Scholar
  24. 24.
    Li, M., Cai, W., Tan, Z.: A region-based multi-sensor image fusion scheme using pulse-coupled neural network. Pattern Recogn. Lett. 27, 1948–1956 (2006)CrossRefGoogle Scholar
  25. 25.
    Zafar, I., Edirisinghe, E.A., Bez, H.E.: Multi-exposure & multi-focus image fusion in transform domain. In: IET International Conference on Visual Information Engineering (2006), pp. 606–611Google Scholar
  26. 26.
    Wei, S., Ke, W.: A multi-focus image fusion algorithm with DT-CWT. In: International Conference on Computational Intelligence and Security (2007), pp. 147–151Google Scholar
  27. 27.
    Saeedi, J., Faez, K., Mozaffari, S.: Multi-focus image fusion based on fuzzy and wavelet transform. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, vol. 5856, pp. 970–977 (2009)Google Scholar
  28. 28.
    Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: Real-time fusion of multi-focus images for visual sensor networks. In: 6th Iranian Conference on Machine Vision and Image Processing (2010), pp. 1–6Google Scholar
  29. 29.
    Yang, Y.: A novel DWT based multi-focus image fusion method. In: International Conference on Advances in Engineering, Netherlands, pp. 177–181 (2011)Google Scholar
  30. 30.
    Phamila, Y.A., Amutha, R.: Discrete cosine transform based fusion of multi-focus images for visual sensor networks. Sig. Process. 95, 161–170 (2014)CrossRefGoogle Scholar
  31. 31.
    Jiang, Q., Jin, X., Lee, S.J., Yao, S.: A novel multi-focus image fusion method based on stationary wavelet transform and local features of fuzzy sets. IEEE Access 5, 20286–20302 (2017)CrossRefGoogle Scholar
  32. 32.
    Zhao, W., Wang, D., Lu, H.: Multi-focus image fusion with a natural enhancement via joint multi-level deeply supervised convolutional neural network. IEEE Trans. Circ. Syst. Videos Technol. (online available) (2018, in press)Google Scholar
  33. 33.
    Yang, Y., Yang, M., Huang, S., Ding, M., Sun, J.: Robust sparse representation combined with adaptive PCNN for multifocus image fusion. IEEE Access 6, 20138–201351 (2018)CrossRefGoogle Scholar
  34. 34.
    Farid, M.S., Mahmood, A., Al-Maadeed, S.A.: Multi-focus image fusion using content adaptive blurring. Inf. Fusion 45, 96–112 (2019)CrossRefGoogle Scholar
  35. 35.
    Aymaz, S., Kose, C.: A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion. Inf. Fusion 45, 113–127 (2019)CrossRefGoogle Scholar
  36. 36.
    Balasubramaniam, P., Ananthi, V.P.: Image fusion using intuitionistic fuzzy sets. Inf. fusion 20, 21–30 (2014)CrossRefGoogle Scholar
  37. 37.
    Swathi, N., Bindu, E., Naidu, V.P.: Pixel level image fusion using fuzzylet fusion algorithm. Int. J. Adv. Res. Electr., Electr. Instrum. Eng. pp. 261–269 (2013)Google Scholar
  38. 38.
    Nazir, A., Ashraf, R., Hamdani, T., Ali, N.: Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor. In: International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–6 (2018)Google Scholar
  39. 39.
    Qayyum, H., Majid, M., Anwar, S.M., Khan, B.: Facial expression recognition using stationary wavelet transform features. Math. Prob. Eng. (2017)Google Scholar
  40. 40.
    Singh, D., Garg, D., Singh Pannu, H.: Efficient landsat image fusion using fuzzy and stationary discrete wavelet transform. The Imaging Sci. J. 17, 108–114 (2017)CrossRefGoogle Scholar
  41. 41.
    Jin, X., Jiang, Q., Yao, S., Zhou, D., Nie, R., Lee, S.J., He, K.: Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain. Infrared Phys. Technol. 88, 1–2 (2018)CrossRefGoogle Scholar
  42. 42.
    Helonde, M.R., Joshi, M.R.: Image fusion based on medical images using DWT and PCA methods. Int. J. Comput. Tech. 2, 75–79 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chitkara University School of Engineering and TechnologyChitkara UniversitySolanIndia
  2. 2.Department of Virtualization, School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunIndia

Personalised recommendations