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Applied Optics

Applied Optics


  • Vol. 41, Iss. 23 — Aug. 10, 2002
  • pp: 4812–4824

Parallel image restoration with a two-dimensional likelihood-based algorithm

Mark A. Neifeld and Yong Wu  »View Author Affiliations

Applied Optics, Vol. 41, Issue 23, pp. 4812-4824 (2002)

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We describe a pixelwise parallel algorithm for the restoration of images that have been corrupted by a low-pass optical channel and additive noise. This new algorithm is based on an iterative soft-decision method of error correction (i.e., turbo decoding) and offers performance on binary-valued imagery that is comparable to the Viterbi algorithm. We quantify the restoration performance of this new algorithm on random binary imagery for which it is superior to both the Wiener filter and the projection onto convex sets algorithms over a wide range of channels. For typical optical channels, the new algorithm is within 0.5 dB of the two-dimensional Viterbi restoration method [J. Opt. Soc. Am. A 17, 265 (2000)]. We also demonstrate the extension of our new algorithm to correlated and gray-scale images using vector quantization to mitigate the associated complexity burden. A highly parallel focal-plane implementation is also discussed, and a design study is presented to quantify the capabilities of such a VLSI hardware solution. We find that video-rate restoration on 252 × 252 pixel images is possible using current technology.

© 2002 Optical Society of America

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
(200.3050) Optics in computing : Information processing
(200.4960) Optics in computing : Parallel processing

Original Manuscript: October 4, 2001
Revised Manuscript: April 11, 2002
Published: August 10, 2002

Mark A. Neifeld and Yong Wu, "Parallel image restoration with a two-dimensional likelihood-based algorithm," Appl. Opt. 41, 4812-4824 (2002)

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