Pattern Selective Image Fusion for Multi-focus Image Reconstruction

  • Vivek Maik
  • Jeongho Shin
  • Joonki Paik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3691)


This paper presents a method for fusing multiple images of a static scene and shows how to apply the proposed method to extend depth of field. Pattern selective image fusion provides a mechanism for combining multiple monochromatic images through identifying salient features in the source images and combining those features in to a single fused image. The source images are first decomposed using filter subtract decimate (FSD) in laplacian domain. Thesum-modified-Laplacian (SML) is used for obtaining the depth of focus in the source images. The selected images are then blended together using monotonically decreasing soft decision blending (SDB), which enables smooth transitions across region boundaries. The resulting fused image utilizes focus information that is greater than that of the constituent images, while retaining a natural verisimilitude. Experimental results show the performance of the depth of focus extension using consumer video camera outputs.


Discrete Cosine Transformation Image Fusion Source Image Saliency Function Automatic Gain Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, J.K.: Multisensor Fusion for Computer Vision. Springer, Berlin (1993)zbMATHGoogle Scholar
  2. 2.
    Akerman, A.: Pyramid techiniques for multisensor fusion. In: Proc. SPIE, vol. 2828, pp. 124–131 (1992)Google Scholar
  3. 3.
    Zhang, Z., Blum, R.S.: A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. In: Proc. IEEE, vol. 87(8), pp. 1315–1326 (1999)Google Scholar
  4. 4.
    Kim, S.K., Park, S.R., Paik, J.K.: Simultaneous out-of-focus blur estimation and restoration for digital AF system. IEEE Trans. Consumer Electronics 44(3), 1071–1075 (1998)CrossRefGoogle Scholar
  5. 5.
    Ligthart, G., Groen, F.: A comparison of different autofocus algorithms. In: IEEE Int. Conf. Pattern Recognition, pp. 597–600 (1992)Google Scholar
  6. 6.
    Aizawa, K., Kodama, K., Kubota, A.: Producing object-based special effects by fusing multiple differently focused images. IEEE Trans. Circuits, Systems for Video Technology 10(2), 323–330 (2000)CrossRefGoogle Scholar
  7. 7.
    Yang, X., Yang, W., Pei, J.: Different focus points images fusion based on wavelet decomposition. In: Proc. Int. Conf. Information Fusion, pp. 3–8 (2000)Google Scholar
  8. 8.
    Li, S., Kwok, J.T., Wang, Y.: Combination of images with diverse focuses using the spatial frequency. Int. Journal, Information Fusion 2(3), 169–176 (2001)CrossRefGoogle Scholar
  9. 9.
    Seales, W.B., Dutta, S.: Everywhere-in-focus image fusion using controllable cameras. In: Proc. SPIE, vol. 2905, pp. 227–234 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Vivek Maik
    • 1
  • Jeongho Shin
    • 1
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia and FilmChung-Ang UniversitySeoulKorea

Personalised recommendations