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A multifocus image fusion using highlevel DWT components and guided filter

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Abstract

It is often difficult and essential to distinguish between focused and de-focused structures in an image. To properly handle such structures, an image fusion technique is developed for multifocus images using high level discrete wavelet components and guided filter. The source images are decomposed using wavelet transform and high level components are processed using gradient magnitude and guided filters to obtain fusion weights to refine the fusion process. Variety of images obtained from standard datasets are used in the simulations to test performance of proposed technique. The fused image obtained using proposed technique outperforms visually and quantitatively as compared to existing techniques.

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References

  1. Amin-Naji M, Aghagolzadeh A (2018) Multi-focus image fusion in DCT domain using variance and energy of Laplacian and correlation coefficient for visual sensor networks. Journal of AI and Data Mining 6(2):233–50

    Google Scholar 

  2. Bavirisetti DP, Dhuli R (2016) Multi-focus image fusion using multi-scale image decomposition and saliency detection. Ain Shams Engineering Journal

  3. Cai J, Cheng Q, Peng M, Song Y (2017) Fusion of infrared and visible images based on nonsubsampled contourlet transform and sparse k-SVD dictionary learning. Infrared Phys Technol 82:85–95

    Article  Google Scholar 

  4. Chaudhary V, Kumar V (2018) Block-based image fusion using multi-scale analysis to enhance depth of field and dynamic range. Signal Image Video Process 12 (2):271–9

    Article  Google Scholar 

  5. Dogra A, Goyal B, Agrawal S (2017) From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE Access 5:16040–67

    Article  Google Scholar 

  6. Du J, Li W, Xiao B, Nawaz Q (2016) Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194:326–39

    Article  Google Scholar 

  7. Garnica-Carrillo A, Calderon F, Flores J (2018) Multi-focus image fusion by local optimization over sliding windows. Signal Image Video Process: 1–8

  8. Haghighat M, Razian MA (2014) Fast-FMI: non-reference image fusion metric. In: 2014 IEEE 8th international conference on application of information and communication technologies (AICT). IEEE, pp 1–3

  9. Kumar BS (2015) Image fusion based on pixel significance using cross bilateral filter. Signal Image Video Process 9(5):1193–204

    Article  Google Scholar 

  10. Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–75

    Article  Google Scholar 

  11. Liu S, Chen J (2016) A fast multi-focus image fusion algorithm by DWT and focused region decision map. In: Signal and information processing association annual summit and conference (APSIPA), 2016 Asia-Pacific. IEEE, pp 1–7

  12. Ma J, Liang P, Yu W, Chen C, Guo X, Wu J, Jiang J (2020) Infrared and visible image fusion via detail preserving adversarial learning. Inform Fusion 54:85–98

    Article  Google Scholar 

  13. Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: a survey. Inform Fusion 45:153–78

    Article  Google Scholar 

  14. Ma J, Yu W, Liang P, Li C, Jiang J (2019) FusionGAN: a generative adversarial network for infrared and visible image fusion. Inform Fusion 48:11–26

    Article  Google Scholar 

  15. Mustafa HT, Liu F, Yang J, Khan Z, Huang Q (2019) Dense multi-focus fusion net: a deep unsupervised convolutional network for multi-focus image fusion. In: International conference on artificial intelligence and soft computing. Springer, Cham, pp 153–163

    Chapter  Google Scholar 

  16. Nejati M, Samavi S, Shirani S (2015) Multi-focus image fusion using dictionary-based sparse representation. Inform Fusion 25:72–84

    Article  Google Scholar 

  17. Nie L, Wang M, Zha ZJ, Chua TS (2012) Oracle in image search: a content-based approach to performance prediction. ACM Trans Inform Sys (TOIS) 30 (2):13

    Google Scholar 

  18. Nie L, Yan S, Wang M, Hong R, Chua TS (2012) Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM international conference on multimedia. ACM, pp 59–68

  19. Pajares G, De La Cruz JM (2004) A wavelet-based image fusion tutorial. Pattern Recogn 37(9):1855–72

    Article  Google Scholar 

  20. Paul S, Sevcenco IS, Agathoklis P (2016) Multi-exposure and multi-focus image fusion in gradient domain. J Circ Sys Comput 25(10):1650123

    Article  Google Scholar 

  21. Yang C, Zhang JQ, Wang XR, Liu X (2008) A novel similarity based quality metric for image fusion. Inform Fusion 9(2):156–60

    Article  Google Scholar 

  22. Yang Y, Que Y, Huang SY, Lin P (2017) Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network. Signal Image Video Process 11(3):439–46

    Article  Google Scholar 

  23. Zhan K, Teng J, Li Q, Shi J (2015) A novel explicit multi-focus image fusion method. Journal of Information Hiding and Multimedia Signal Processing 6(3):600–12

    Google Scholar 

  24. Zhan K, Xie Y, Wang H, Min Y (2017) Fast filtering image fusion. J Electron Imaging 26(6):063004

    Article  Google Scholar 

  25. Zhang Y (2015) Multi-focus image fusion based on sparse decomposition. Int J Signal Process Image Process Pattern Recogn 8(2):157–64

    Google Scholar 

  26. Zhang Y, Bai X, Wang T (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inform Fusion 35:81–101

    Article  Google Scholar 

  27. Zhang Y, Chen L, Jia J, Zhao Z (2014) Multi-focus image fusion based on non-negative matrix factorization and difference images. Signal Process 105:84–97

    Article  Google Scholar 

  28. Zhang Y, Wei W, Yuan Y (2018) Multi-focus image fusion with alternating guided filtering. Signal Image Video Process: 1–9

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    M. Munawwar Iqbal Ch, Naima Iltaf & Abdul Ghafoor

  2. COMSATS University, Islamabad, Pakistan

    M. Mohsin Riaz & Syed Sohaib Ali

Authors
  1. M. Munawwar Iqbal Ch
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  2. M. Mohsin Riaz
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  3. Naima Iltaf
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  4. Abdul Ghafoor
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  5. Syed Sohaib Ali
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Correspondence to M. Mohsin Riaz.

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Ch, M.M.I., Riaz, M.M., Iltaf, N. et al. A multifocus image fusion using highlevel DWT components and guided filter. Multimed Tools Appl 79, 12817–12828 (2020). https://doi.org/10.1007/s11042-020-08661-8

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  • DOI: https://doi.org/10.1007/s11042-020-08661-8

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