Taj-Shanvi Framework for Image Fusion Using Guided Filters

  • Uma N. DulhareEmail author
  • Areej Mohammed Khaleed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1016)


Multi-focus image fusion aims to produce an all-in-focus image by integrating a series of partially focused images of the same scene. A small defocused (focused) region is usually encompassed by a large focused (defocused) region in the partially focused image; however, many state-of-the-art fusion methods cannot correctly distinguish this small region. To solve this problem, a novel Taj-Shanvi framework, used for multi-focus image fusion algorithm based on multi-scale focus measures and generalized random walk (GRW), is implemented. First, multi-scale decision maps are obtained with multi-scale focus measures. Then, multi-scale guided filters are used to make the decision maps accurately align with the boundaries between focused and defocused regions. Next, GRW is used to combine these decision maps at different scales. After obtaining them, these maps are aligned using the watershed technique, whose edges are further smoothed using the guided filter. Experimental results are obtained by using few quality parameters, namely, entropy, edge structure-based similarity index measure, spatial frequency, mutual information, and so on, to evaluate the quality of the final fused image. Quality parameter assessment demonstrates that the proposed method produces a better quality fused image than conventional image fusion techniques.


Multi-focus Image fusion Guided filter Watershed technique 


  1. 1.
  2. 2.
    Pajares, G. (2004). A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855–1872. CrossRefGoogle Scholar
  3. 3.
    Bai, X., Zhang, Y., Zhou, F., & Bindang. (2015). Quadtree-based multi- focus image fusion using a weighted focus-measure. Information Fusion, 22,105–118.Google Scholar
  4. 4.
    Zhang, Y., Bai, X., Wang, T. (2017). Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Information Fusion.Google Scholar
  5. 5.
    Nayar, S. K., & Nakagawa, Y. (1994). Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8), 824–831.CrossRefGoogle Scholar
  6. 6.
    Shen, R., Cheng, I., Shi, J., et al. (2011). Generalized random walks for fusion of multi- exposure images. IEEE Transactions on Image Processing, 20(12), 3634–3646.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Li, S., Kang, X., & Hu, J. (2013). Image fusion with guided filtering. IEEE Transactions on Image Processing, 22(7), 2864–2875.CrossRefGoogle Scholar
  8. 8.
    Sarker, M. S. Z., Haw, T. W., & Logeswaran, R.: Morphological based technique for image segmentation. International Journal of Information Technology, 14(1).Google Scholar
  9. 9.
    Bhagwat, M., Krishna, R. K., & Pise, V. (2010). Simplified watershed transformation. International Journal of Computer Science and Communication, 1(1), 175–177.Google Scholar
  10. 10.
    Nejati, M., Samavi, S., & Shirani, S. (2015). Multi-focus image fusion using dictionary-based sparse representation. Information Fusion, 25, 72–84.CrossRefGoogle Scholar
  11. 11.
    Yang, L., Guo, B. L., Ni, W. (2008). Multimodality medical image fusion based on multi-scale geometric analysis of Contourlet transform. Euro Computing, 72, 203211.CrossRefGoogle Scholar
  12. 12.
    Singh, S., Gyaourova, A., Bebis, G.., & Pavlidis, I. (2004). Infrared and visible image fusion for face recognition. Proc. SPIE, 5404, 585596.Google Scholar
  13. 13.
    Kaur, P., & Sharma, E. R. (2015). A study of various multi-focus image fusion techniques. International Journal of Computer Science and Mobile Computing, 4(6).Google Scholar
  14. 14.
    Grady, L. (2006). Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), 1768–1783.CrossRefGoogle Scholar
  15. 15.
    Amoda, N., & Kulkarni, R. (2013). Image segmentation and detection using watershed transform and region based image retrieval. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),2(2).Google Scholar
  16. 16.
    Sivagami, R., Vaithiyanathan, V., Sangeetha, V., Ifjaz, M., Ahmed, K., Sundar, J. A., et al. (2015). Review of image fusion techniques and evaluation metrics for remote sensing applications. Indian Journal of Science and Technology, 8(35),, December 2015.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.C.S.E. DepartmentMuffakham Jah College of Engineering and TechnologyHyderabadIndia

Personalised recommendations