OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 45, Iss. 13 — May. 1, 2006
  • pp: 2859–2870

Benefits of optical system diversity for multiplexed image reconstruction

Hseuh-Ban Lan, Sally L. Wood, Marc P. Christensen, and Dinesh Rajan  »View Author Affiliations

Applied Optics, Vol. 45, Issue 13, pp. 2859-2870 (2006)

View Full Text Article

Enhanced HTML    Acrobat PDF (963 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Algorithms that use optical system diversity to improve multiplexed image reconstruction from multiple low-resolution images are analyzed and demonstrated. Compared with systems using identical imagers, systems using additional lower-resolution imagers can have improved accuracy and computation. The diverse system is not sensitive to boundary conditions and can take full advantage of improvements that decrease noise and allow an increased number of bits per pixel to represent spatial information in a scene.

© 2006 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
(110.0110) Imaging systems : Imaging systems

ToC Category:
Image Reconstruction

Original Manuscript: August 24, 2005
Manuscript Accepted: October 25, 2005

Hseuh-Ban Lan, Sally L. Wood, Marc P. Christensen, and Dinesh Rajan, "Benefits of optical system diversity for multiplexed image reconstruction," Appl. Opt. 45, 2859-2870 (2006)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. S. Chaudhuri, ed. Super-Resolution Imaging (Kluwer Academic, 2001).
  2. 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]
  3. 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]
  4. N. Nguyen, P. Milanfar, and G. H. Golub, "A computationally efficient image superresolution algorithm," IEEE Trans. Image Process. 10, 573-583 (2001). [CrossRef]
  5. M. K. Ng and N. K. Bose, "Mathematical analysis of super-resolution methodology," IEEE Signal Process. Mag. 20, 62-74 (2003). [CrossRef]
  6. S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Fast and robust multi-frame super-resolution," IEEE Trans. Image Process. 13, 1327-1344 (2004). [CrossRef] [PubMed]
  7. J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Morimoto, N. Kondou, D. Miyazaki, and Y. Ichioka, "Thin observation module by bound optics (TOMBO): concept and experimental verification," Appl. Opt. 40, 1806-1813 (2001). [CrossRef]
  8. Y. Kitamura, R. Shogenji, K. Yamada, S. Miyatake, M. Miyamoto, T. Morimoto, Y. Masaki, N. Kondou, D. Miyazaki, J. Tanida, and Y. Ichioka, "Reconstruction of a high resolution image on a compound-eye image capturing system," Appl. Opt. 43, 1719-1727 (2004). [CrossRef] [PubMed]
  9. M. P. Christensen, M. W. Haney, D. Rajan, S. L. Wood, and S. C. Douglas, "PANOPTES: a thin agile multi-resolution imaging sensor," Presented at the Government Microcircuit Applications and Critical Technology Conference (GOMACTech-05), Las Vegas, Nev., 4-7 April 2005 paper 21.5.
  10. M. P. Christensen, V. Bhakta, D. Rajan, S. C. Douglas, S. L. Wood, and M. W. Haney, "Adaptive flat multiresolution multiplexed computational imaging architecture utilizing micromirror arrays to steer subimager field of views," Appl. Opt. 45, 2884-2892 (2006). [CrossRef] [PubMed]
  11. S. L. Wood, B. J. Smithson, M. P. Christensen, and D. Rajan, Rerformance of a MVE algorithm for compound eye image reconstruction using lens diversity," in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP' 05) (IEEE, 2005), Vol. II. pp. 593-596.
  12. R. N. Bracewell, Two-Dimensional Imaging (Prentice-Hall, 1995).
  13. A. Macovski, Medical Imaging Systems (Prentice-Hall, 1983).
  14. T. Kailath, Linear Systems (Prentice-Hall, 1980).
  15. A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, 1989), Chap. 2.
  16. R. M. Gray, "On the asympototic eigenvalue distribution of Toeplitz matrices," IEEE Trans. Inf. Theory IT-18, 725-730 (1972). [CrossRef]
  17. S. L. Wood, D. Rajan, M. P. Christensen, S. C.Douglas, and B. J. Smithson, "Resolution improvement for compound eye images through lens diversity," in Digital Signal Processing Workshop 2004 and the Third IEEE Signal Processing Education Workshop (IEEE, 2004), pp. 151-155, doi: . [CrossRef]
  18. M. Born and E. Wolf, Principles of Optics (Cambridge U. Press, 1959).
  19. X. Ying and Z. Hu, "Distortion correction of fisheye lens using a nonparametric imaging model," in Proceedings of Asian Conference on Computer Vision (Asian Federation of Computer Vision Societies 2004), pp. 527-532.
  20. C. Brauer-Burchardt and K. Voss, "A new algorithm to correct fish-eye-and strong wide-angle-lens-distortion from single images," in Proceedings of IEEE International Conference on Image Processing (IEEE, 2001), pp. 225-228.

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