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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 24, Iss. 2 — Feb. 1, 2007
  • pp: 311–325

Digital superresolution and the generalized sampling theorem

Sudhakar Prasad  »View Author Affiliations


JOSA A, Vol. 24, Issue 2, pp. 311-325 (2007)
http://dx.doi.org/10.1364/JOSAA.24.000311


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Abstract

The technique of reconstructing a higher-resolution (HR) image of size M L × M L by digitally processing L × L subpixel-shifted lower-resolution (LR) copies of it, each of size M × M , has now become well established. This particular digital superresolution problem is analyzed from the standpoint of the generalized sampling theorem. It is shown both theoretically and by computer simulation that the choice of regularly spaced subpixel shifts for the LR images tends to maximize the robustness and minimize the error of reconstruction of the HR image. In practice, since subpixel-level control of LR image shifts may be nearly impossible to achieve, however, a more likely scenario, which is also discussed, is one involving random subpixel shifts. It is shown that without reasonably tight bounds on the range of random shifts, the reconstruction is likely to fail in the presence of even small amounts of noise unless either reliable prior information or additional data are available.

© 2007 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution

ToC Category:
Image Processing

History
Original Manuscript: April 17, 2006
Revised Manuscript: August 14, 2006
Manuscript Accepted: August 21, 2006

Citation
Sudhakar Prasad, "Digital superresolution and the generalized sampling theorem," J. Opt. Soc. Am. A 24, 311-325 (2007)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-24-2-311


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