OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 31 — Nov. 1, 2012
  • pp: 7566–7575

Image fusion through feature extraction by using sequentially combined toggle and top-hat based contrast operator

Xiangzhi Bai  »View Author Affiliations

Applied Optics, Vol. 51, Issue 31, pp. 7566-7575 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (979 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Combing the useful information of multisensor or multifocus images is important for producing effective optical images. To extract and combine the image features of the original images for image fusion well, an algorithm through feature extraction by using the sequentially combined toggle and top-hat based contrast operator is proposed in this paper. Sequentially combining toggle contrast operator and top-hat based contrast operator could be used to identify well the effective bright and dark image features. Furthermore, through multiscale extension, the effective bright and dark image features at multiscales of an image are extracted. After the final bright and dark fusion features are constructed by using the pixel-wise maximum operation on the multiscale image features from different images, the final fusion result is obtained by importing the final bright and dark fusion features into the base image. Experimental results on different types of images show that the proposed algorithm performs well for image fusion, which may be widely used in different applications, such as security surveillance, object recognition, and so on.

© 2012 Optical Society of America

OCIS Codes
(070.4690) Fourier optics and signal processing : Morphological transformations
(100.2000) Image processing : Digital image processing
(110.2960) Imaging systems : Image analysis
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Imaging Systems

Original Manuscript: July 31, 2012
Revised Manuscript: September 27, 2012
Manuscript Accepted: September 28, 2012
Published: October 25, 2012

Xiangzhi Bai, "Image fusion through feature extraction by using sequentially combined toggle and top-hat based contrast operator," Appl. Opt. 51, 7566-7575 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. Y. Chen, L. Wang, Z. Sun, Y. Jiang, and G. Zhai, “Fusion of color microscopic images based on bidimensional empirical mode decomposition,” Opt. Express 18, 21757–21769 (2010). [CrossRef]
  2. M. Leviner and M. Maltz, “A new multi-spectral feature level image fusion method for human interpretation,” Infrared Phys. Technol. 52, 79–88 (2009). [CrossRef]
  3. S. E. El-Khamy, M. M. Hadhoud, M. I. Dessouky, B. M. Salam, and F. E. Abd El-Samie, “Blind multichannel reconstruction of high-resolution images using wavelet fusion,” Appl. Opt. 44, 7349–7356 (2005). [CrossRef]
  4. G. Pajares and J. M. Cruz, “A wavelet-based image fusion tutorial,” Pattern Recogn. 37, 1855–1872 (2004). [CrossRef]
  5. K. Amolins, Y. Zhang, and P. Dare, “Wavelet based image fusion techniques—An introduction, review and comparison,” ISPRS J. Photogramm. Remote Sens. 62, 249–263 (2007). [CrossRef]
  6. A. Aran, S. Munshi, V. K. Beri, and A. K. Gupta, “Spectral band invariant wavelet-modified MACH filter,” Opt. Lasers Eng. 46, 656–665 (2008). [CrossRef]
  7. F. E. Ali, I. M. El-Dokany, A. A. Saad, W. Al-Nuaimy, and F. E. Abd El-Samie, “High resolution image acquisition from magnetic resonance and computed tomography scans using the curvelet fusion algorithm with inverse interpolation techniques,” Appl. Opt. 49, 114–125 (2010). [CrossRef]
  8. M. González-Audícana, J. L. Saleta, R. G. Catalán, and R. García, “Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition,” IEEE Trans. Geosci. Remote Sens. 42, 1291–1299 (2004). [CrossRef]
  9. N. Cvejic, D. Bull, and N. Canagarajah, “Region-based multimodal image fusion using ICA bases,” IEEE Sens. J. 7, 743–751 (2007). [CrossRef]
  10. D. M. Bulanona, T. F. Burks, and V. Alchanatis, “Image fusion of visible and thermal images for fruit detection,” Biosyst. Eng. 103, 12–22 (2009). [CrossRef]
  11. S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26, 971–979 (2008). [CrossRef]
  12. A. Toet, M. A. Hogervorst, S. G. Nikolov, J. J. Lewis, T. D. Dixon, D. R. Bull, and C. N. Canagarajah, “Towards cognitive image fusion,” Inform. Fusion 11, 95–113 (2010). [CrossRef]
  13. Z. Wang, Y. Ma, and J. Gu, “Multi-focus image fusion using PCNN,” Pattern Recogn. 43, 2003–2016 (2010). [CrossRef]
  14. W. Huang and Z. Jing, “Multi-focus image fusion using pulse coupled neural network,” Pattern Recogn. Lett. 28, 1123–1132 (2007). [CrossRef]
  15. P. Soille, Morphological Image Analysis—Principle and Applications (Springer, 2003).
  16. J. Serra, Image Analysis and Mathematical Morphology(Academic, 1982).
  17. X. Bai, F. Zhou, and B. Xue, “Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction,” Appl. Opt. 51, 338–347 (2012). [CrossRef]
  18. S. Mukhopadhyay and B. Chanda, “Fusion of 2D grayscale images using multiscale morphology,” Pattern Recogn. 34, 1939–1949 (2001). [CrossRef]
  19. X. Bai, F. Zhou, and B. Xue, “Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform,” Opt. Express 19, 8444–8457 (2011). [CrossRef]
  20. J. Serra, “Toggle mappings,” Technical Report N-18/88/MM (Centre de Morphologie Mathematique, 1988).
  21. L. Dorini and N. Leite, “A scale-space toggle operator for morphological segmentation,” in Proceedings of the 8th International Symposium on Mathematical Morphology (ISMM, 2007), pp. 101–112.
  22. X. Bai and F. Zhou, “Multiscale toggle contrast operator based mineral image enhancement,” J. Microsc. 243, 141–153 (2011). [CrossRef]
  23. X. Bai, “Mineral image enhancement based on sequential combination of toggle and top-hat based contrast operator,” Micron, doi:10.1016/j.micron.2012.06.009 (in press). [CrossRef]
  24. P. Jackway and M. Deriche, “Scale-space properties of the multiscale morphological dilation-erosion,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 38–51 (1996). [CrossRef]
  25. V. Aslantas and R. Kurban, “A comparison of criterion functions for fusion of multi-focus noisy images,” Opt. Commun. 282, 3231–3242 (2009). [CrossRef]
  26. W. Wang, P. Tang, and C. Zhu, “A wavelet transform based image fusion method,” J. Image Graphics 6, 1130–1136 (2001).
  27. P. Pradham, N. H. Younan, and R. L. King, “Concepts of image fusion in remote sensing applications,” in Image Fusion: Algorithms and Applications, T. Stathak, ed. (Academic, 2008), pp. 391–428.
  28. J. Roberts, J. Aardt, and F. Ahmed, “Assessment of image fusion procedures using entropy, image quality, and multispectral classification,” J. Appl. Remote Sens. 2, 023522 (2008). [CrossRef]
  29. Y. Chen, Z. Xue, and R. S. Blum, “Theoretical analysis of an information-based quality measure for image fusion,” Inform. Fusion 9, 161–175 (2008). [CrossRef]
  30. G. Qu, D. Zhang, and P. Yan, “Information measure for performance of image fusion,” Electron. Lett. 38, 313–315 (2002). [CrossRef]
  31. G. Piella and H. Heijmans, “A new quality metric for image fusion,” in Proceedings of International Conference on Image Processing (IEEE, 2003), pp. 173–176.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited