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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 46, Iss. 8 — Mar. 10, 2007
  • pp: 1211–1222

Multiframe superresolution of binary images

Premchandra M. Shankar and Mark A. Neifeld  »View Author Affiliations


Applied Optics, Vol. 46, Issue 8, pp. 1211-1222 (2007)
http://dx.doi.org/10.1364/AO.46.001211


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Abstract

We describe a new algorithm for superresolving a binary object from multiple undersampled low-resolution (LR) images that are degraded by diffraction-limited optical blur, detector blur, and additive white Gaussian noise. Two-dimensional distributed data detection (2D4) is an iterative algorithm that employs a message-passing technique for estimating the object pixel likelihoods. We present a novel non-training-based complexity-reduction technique that makes the algorithm suitable even for channels with support size as large as 5×5 object pixels. We compare the performance and computational complexity of 2D4 with that of iterative backprojection (IBP). In an imaging system with an optical blur spot matched to the object pixel size, 2×2 undersampled measurement pixels, and four LR images, the reconstruction error measured in terms of the number of pixel mismatches for 2D4 is 300 times smaller than that for IBP at a signal-to-noise ratio of 38  dB .

© 2007 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration
(100.6640) Image processing : Superresolution

History
Original Manuscript: June 30, 2006
Manuscript Accepted: September 24, 2006
Published: February 20, 2007

Citation
Premchandra M. Shankar and Mark A. Neifeld, "Multiframe superresolution of binary images," Appl. Opt. 46, 1211-1222 (2007)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-46-8-1211


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