OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 14 — May. 10, 2012
  • pp: 2616–2623

Autofocus for multispectral camera using focus symmetry

Hui-Liang Shen, Zhi-Huan Zheng, Wei Wang, Xin Du, Si-Jie Shao, and John H. Xin  »View Author Affiliations

Applied Optics, Vol. 51, Issue 14, pp. 2616-2623 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (536 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



A multispectral camera acquires spectral color images with high fidelity by splitting the light spectrum into more than three bands. Because of the shift of focal length with wavelength, the focus of each channel should be mechanically adjusted in order to obtain sharp images. Because progressive adjustment is quite time consuming, the clear focus must be determined by using a limited number of images. This paper exploits the symmetry of focus measure distribution and proposes a simple yet efficient autofocus method. The focus measures are computed using first-order image derivatives, and the focus curve is obtained by spline interpolation. The optimal focus position, which maximizes the symmetry of the focus measure distribution, is then computed according to distance metrics. The effectiveness of the proposed method is validated in the multispectral camera system, and it is also applicable to relevant imaging systems.

© 2012 Optical Society of America

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(330.1710) Vision, color, and visual optics : Color, measurement
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Imaging Systems

Original Manuscript: January 3, 2012
Revised Manuscript: February 15, 2012
Manuscript Accepted: March 10, 2012
Published: May 8, 2012

Virtual Issues
Vol. 7, Iss. 7 Virtual Journal for Biomedical Optics

Hui-Liang Shen, Zhi-Huan Zheng, Wei Wang, Xin Du, Si-Jie Shao, and John H. Xin, "Autofocus for multispectral camera using focus symmetry," Appl. Opt. 51, 2616-2623 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. P. L. Vora, J. E. Farrell, J. D. Tietz, and D. H. Brainard, “Image capture: simulation of sensor responses from hyperspectral images,” IEEE Trans. Image Process. 10, 307–316 (2001). [CrossRef]
  2. H. Haneishi, T. Hasegawa, A. Hosoi, Y. Yokoyama, N. Tsumura, and Y. Miyake, “System design for accurately estimating the spectral reflectance of art paintings,” Appl. Opt. 39, 6621–6632 (2000). [CrossRef]
  3. M. A. Lopez-Alvarez, J. Hernandez-Andres, E. M. Volero, and J. Romero, “Selecting algorithm, sensors, and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. A 24, 942–956 (2007). [CrossRef]
  4. J. Gerhardt and J. Y. Hardeberg, “Spectral color reproduction minimizing spectral and perceptual color differences,” Color Res. Appl. 33, 494–504 (2008). [CrossRef]
  5. H. L. Shen, P. Q. Cai, S. J. Shao, and J. H. Xin, “Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation,” Opt. Express 15, 15545–15554 (2007). [CrossRef]
  6. J. Brauers and T. Aach, “Geometric calibration of lens and filter distortions for multispectral filter-wheel cameras,” IEEE Trans. Image Process. 20, 496–505 (2011). [CrossRef]
  7. A. Mansouri, F. S. Marzani, J. Y. Hardeberg, and P. Gouton, “Optical calibration of a multispectral imaging system based on interference filters,” Opt. Eng. 44, 027004 (2005). [CrossRef]
  8. J. Brauers and T. Aach, “Longitudinal aberrations caused by optical filters and their compensation in multispectral imaging,” in IEEE International Conference on Image Processing (ICIP) (IEEE, 2008), pp. 525–528.
  9. C. M. Chen, C. M. Hong, and H. C. Chuang, “Efficient auto-focus algorithm utilizing discrete difference equation prediction model for digital still cameras,” IEEE Trans. Consum. Electron. 52, 1135–1143 (2006). [CrossRef]
  10. S. Y. Lee, Y. Kumar, J. M. Cho, S. W. Lee, and S. W. Kim, “Enhanced autofocus algorithm using robust focus measure and fuzzy reasoning,” IEEE Trans. Circuits Syst. Video Technol. 18, 1237–1246 (2008). [CrossRef]
  11. K. R. Park and J. Kim, “A real-time focusing algorithm for iris recognition camera,” IEEE Trans. Syst. Man Cybern. 35, 441–444 (2005). [CrossRef]
  12. Y. Sun, S. Duthaler, and B. J. Nelson, “Autofocusing in computer microscopy: selecting the optimal focus algorithm,” Microsc. Res. Tech. 65, 139–149 (2004). [CrossRef]
  13. K. S. Choi, J. S. Lee, and S. J. Ko, “New autofocusing technique using the frequency selective weighted median filter for video cameras,” IEEE Trans. Consum. Electron. 45, 820–827(1999). [CrossRef]
  14. L. C. Chiu and C. S. Fuh, “An efficient auto focus method for digital still camera based on focus value curve prediction model,” J. Inf. Sci. Eng. 26, 1261–1272 (2010).
  15. M. Subbarao and J. K. Tyan, “Selecting the optimal focus measure for autofocusing and depth-from-focus,” IEEE Trans. Patt. Anal. Mach. Intell. 20, 864–870 (1998). [CrossRef]
  16. S. O. Shim and T. S. Choi, “A novel iterative shape from focus algorithm based on combinatorial optimization,” Patt. Recogn. 43, 3338–3347 (2010). [CrossRef]
  17. W. Huang and Z. Jing, “Evaluation of focus measures in multi-focus image fusion,” Patt. Recogn. Lett. 28, 493–500 (2007). [CrossRef]
  18. J. M. Tenenbaum, “Accommodation in computer vision,” Ph.D. thesis (Stanford University, 1970).
  19. S. K. Nayar and Y. Nakagawa, “Shape from focus,” IEEE Trans. Patt. Anal. Mach. Intell. 16, 824–831 (1994). [CrossRef]
  20. K. Ooi, K. Izumi, M. Nozaki, and I. Takeda, “An advanced auto-focus system for video camera using quasi-condition reasoning,” IEEE Trans. Consum. Electron. 36, 526–530 (1990). [CrossRef]
  21. J. He, R. Zhou, and Z. Hong, “Modified fast climbing search auto-focus algorithm with adaptive step size searching technique for digital camera,” IEEE Trans. Consum. Electron. 49, 257–262 (2003). [CrossRef]
  22. A. P. Pentland, “A new sense for depth of field,” IEEE Trans. Patt. Anal. Mach. Intell. PAMI-9, 523–531 (1987). [CrossRef]
  23. A. Aslantas, “A depth estimation algorithm with a single image,” Opt. Express 15, 5024–5029 (2007). [CrossRef]
  24. M. Subbarao, “Depth recovery from blurred edges,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 1988), pp. 498–503.
  25. M. Swain and D. Ballard, “Color indexing,” Int. J. Comput. Vis. 7, 11–32 (1991). [CrossRef]
  26. S. Kullback and R. A. Leibler, “On information and sufficiency,” Ann. Math. Stat. 22,79–86 (1951). [CrossRef]
  27. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).
  28. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet: sRGB,” Version 1.10, ICC (1996).
  29. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004). [CrossRef]

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