OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 45, Iss. 13 — May. 1, 2006
  • pp: 2871–2883

Multiaperture imaging

Premchandra M. Shankar, William C. Hasenplaugh, Rick L. Morrison, Ronald A. Stack, and Mark A. Neifeld  »View Author Affiliations

Applied Optics, Vol. 45, Issue 13, pp. 2871-2883 (2006)

View Full Text Article

Enhanced HTML    Acrobat PDF (3543 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



We study the reconstruction of a high-resolution image from multiple low-resolution images by using a nonlinear iterative backprojection algorithm. We exploit diversities in the imaging channels, namely, the number of imagers, magnification, position, rotation, and fill factor, to undo the degradation caused by the optical blur, pixel blur, and additive noise. We quantify the improvements gained by these diversities in the reconstruction process and discuss the trade-off among system parameters. As an example, for a system in which the pixel size is matched to the diffraction-limited optical blur size at a moderate detector noise level, we can reduce the reconstruction root-mean-square error by 570 % by using 16 cameras and a large amount of diversity. The algorithm was implemented on a 56 camera array specifically constructed to demonstrate the resolution-enhancement capabilities. Practical issues associated with building and operating this device are presented and analyzed.

© 2006 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.3020) Image processing : Image reconstruction-restoration
(100.6640) Image processing : Superresolution
(110.0110) Imaging systems : Imaging systems
(110.1220) Imaging systems : Apertures

ToC Category:
Image Reconstruction

Original Manuscript: August 10, 2005
Revised Manuscript: October 27, 2005
Manuscript Accepted: October 23, 2005

Premchandra M. Shankar, William C. Hasenplaugh, Rick L. Morrison, Ronald A. Stack, and Mark A. Neifeld, "Multiaperture imaging," Appl. Opt. 45, 2871-2883 (2006)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. K. Aizawa, T. Komatsu, and T. Saito, "A scheme for acquiring very high resolution images using multiple cameras," IEEE Trans. Acoust. Speech Signal Process. 3, 23-26 (1992).
  2. S. Chaudhuri, ed., Super-Resolution Imaging (Norwell, 2001).
  3. R. Y. Tsai and T. S. Huang, "Multiframe image restoration and registration," in Advances-Computer Vision and Image Process (JAI Press, 1984), Vol. 1, pp. 317-339.
  4. H. Ur and D. Gross, "Improved resolution from subpixel shifted pictures," CVGIP: Graph. Models Image Process. 54, 181-186 (1992). [CrossRef]
  5. M. Irani and S. Peleg, "Improving resolution by image registration," CVGIP: Graph. Models Image Process. 53, 231-239 (1991). [CrossRef]
  6. M. Elad and A. Feuer, "Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images," IEEE Trans. Image Process. 6, 1646-1658 (1997). [CrossRef] [PubMed]
  7. R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, and E. A. Watson, "High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system," Opt. Eng. 37, 247-260 (1998). [CrossRef]
  8. S. Baker and T. Kanade, "Limits on super-resolution and how to break them," IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002). [CrossRef]
  9. C. A. Segall, R. Molina, and A. K. Katsaggelos, "High-resolution images from low-resolution compressed video," IEEE Signal Process. Mag. 20, 37-48 (2003). [CrossRef]
  10. J. S. Lim, Two-Dimensional Signal and Image Processing (Prentice-Hall, 1990), pp. 495-510.
  11. D. Gesbert, P. Duhamel, and S. Mayrargue, "On-line blind multichannel equalization based on mutually referenced filters," IEEE Trans. Signal Process. 45, 2307-2317 (1997). [CrossRef]
  12. G. B. Giannakis and C. Tepedelenlioglu, "Direct blind equalizers of multiple FIR channels: a deterministic approach," IEEE Trans. Signal Process. 47, 62-74 (1999). [CrossRef]
  13. J. W. Goodman, Statistical Optics (Wiley, 1985), pp. 85-89.
  14. A. J. den Dekker and A. van den Bos, "Resolution: a survey," J. Opt. Soc. Am. A 14, 547-557 (1997). [CrossRef]
  15. M. A. Neifeld, "Information, resolution, and space-bandwidth product," Opt. Lett. 23, 1477-1479 (1998). [CrossRef]
  16. P. Milanfar and A. Shakouri, "A statistical analysis of diffraction-limited imaging," In IEEE 2000 International Conference on Image Processing (ICIP-2002) (IEEE, 2002), Vol. 1, pp. 22-25.
  17. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C++: the Art of Scientific Computing (Cambridge U. Press, 2002), pp. 108-132.
  18. M. Chiang and T. Boult, "Efficient image warping and super-resolution," in Proceedings of the Third IEEE Workshop on Applications of Computer Vision (IEEE, 1996), pp. 56-61. [CrossRef]
  19. M. C. Chiang and T. E. Boult, "Local blur estimation and super-resolution," in Proceedings of Computer Vision and Pattern Recognition, IEEE Computer Society Conference (IEEE 1997), pp. 821-826.

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