OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 24 — Aug. 20, 2014
  • pp: F1–F9

Efficient multiframe super-resolution for imagery with lateral shifts

Drew P. Kouri and Eric A. Shields  »View Author Affiliations

Applied Optics, Vol. 53, Issue 24, pp. F1-F9 (2014)

View Full Text Article

Enhanced HTML    Acrobat PDF (534 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Trade studies used to design optical imaging systems frequently result in systems being undersampled. The resolution of such systems is limited by the finite size of the detector pixels rather than the cutoff spatial frequency of the optical system. Multiframe super-resolution techniques can be used to combine a number of spatially displaced images from such systems into a single, high-resolution image. Nonlinear optimization techniques have frequently been used to solve this problem. Such techniques define an objective function and use numerical optimization methods to obtain a solution. These numerical methods are often more efficient when derivatives of the objective function with respect to the variables can be calculated analytically rather than numerically. We demonstrate for the simple motion model of pure lateral translation that the derivatives of the objective function with respect to the image lateral shifts can be calculated analytically to speed optimization calculations.

© 2014 Optical Society of America

OCIS Codes
(000.3870) General : Mathematics
(100.2000) Image processing : Digital image processing
(100.6640) Image processing : Superresolution

Original Manuscript: March 3, 2014
Revised Manuscript: June 4, 2014
Manuscript Accepted: June 22, 2014
Published: August 1, 2014

Virtual Issues
Vol. 9, Iss. 10 Virtual Journal for Biomedical Optics

Drew P. Kouri and Eric A. Shields, "Efficient multiframe super-resolution for imagery with lateral shifts," Appl. Opt. 53, F1-F9 (2014)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. R. D. Fiete, “Image quality and λFN/p for remote sensing systems,” Opt. Eng. 38, 1229–1240 (1999). [CrossRef]
  2. A. S. Fruchter and R. N. Hook, “A novel image reconstruction method applied to deep Hubble space telescope images,” Proc. SPIE 3164, 120–125 (1997). [CrossRef]
  3. M. S. Alam, J. G. Bognar, S. Cain, and B. J. Yasuda, “Fast registration and reconstruction of aliased low-resolution frames by use of a modified maximum-likelihood approach,” Appl. Opt. 37, 1319–1328 (1998). [CrossRef]
  4. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process. 13, 1327–1344 (2004). [CrossRef]
  5. B. C. Tom and A. K. Katsaggelos, “Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images,” in Proceedings of the International Conference on Image Processing (1995), Vol. I–III, pp. B539–B542.
  6. V. Bannore and L. Swierkowski, “An iterative approach to image super-resolution,” in Intelligent Information Processing III, Z. Shi, K. Shimohara, and D. Feng, eds. (Springer, 2006), pp. 473–482.
  7. H. Stark and P. Oskoui, “High-resolution image recovery from image-plane arrays, using convex projections,” J. Opt. Soc. Am. A 6, 1715–1726 (1989). [CrossRef]
  8. L. Pickup, S. Roberts, A. Zisserman, and D. Capel, “Multiframe super-resolution from a Bayesian perspective,” in Super-Resolution Imaging, P. Milanfar, ed. (CRC Press, 2011), pp. 247–284.
  9. S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003). [CrossRef]
  10. J. Tian and K. K. Ma, “A survey on super-resolution imaging,” Signal Image Video Process. 5, 329–342 (2011).
  11. R. C. Hardie, K. J. Barnard, and E. E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997). [CrossRef]
  12. Q. Wang and X. Song, “Joint image registration and super-resolution reconstruction based on regularized total least norm,” in 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1537–1540.
  13. M. Vrigkas, C. Nikou, and L. P. Kondi, “Accurate image registration for MAP image super-resolution,” Signal Process. Image Commun. 28, 494–508 (2013). [CrossRef]
  14. J. Nocedal and S. J. Wright, Numerical Optimization, 2nd ed. (Springer, 2006).
  15. R. Fletcher, Practical Methods of Optimization, 2nd ed. (Wiley, 1987).
  16. D. S. C. Biggs and M. Andrews, “Acceleration of iterative image restoration algorithms,” Appl. Opt. 36, 1766–1775 (1997). [CrossRef]
  17. 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]
  18. M. Guizar-Sicairos, S. T. Thurman, and J. R. Fienup, “Efficient subpixel image registration algorithms,” Opt. Lett. 33, 156–158 (2008). [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.


Fig. 1. Fig. 2. Fig. 3.
Fig. 4.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited