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Proceedings Paper

Constrained least-squares image restoration filters for sampled image data
Author(s): Rajeeb Hazra; Stephen K. Park; G. Louis Smith; Stephen E. Reichenbach
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Paper Abstract

Constrained least-squares image restoration, first proposed by Hunt twenty years ago, is a linear image restoration technique in which the smoothness of the restored image is maximized subject to a constraint on the fidelity of the restored image. The traditional derivation and implementation of the constrained least-squares restoration (CLS) filter is based on an incomplete discrete/discrete (d/d) system model which does not account for the effects of spatial sampling and image reconstruction. For many imaging systems, these effects are significant and should not be ignored. In a 1990 SPIE paper, Park et. al. demonstrated that a derivation of the Wiener filter based on the incomplete d/d model can be extended to a more comprehensive end-to-end, continuous/discrete/continuous (c/d/c) model. In a similar 1992 SPIE paper, Hazra et al. attempted to extend Hunt's d/d model-based CLS filter derivation to the c/d/c model, but with limited success. In this paper, a successful extension of the CLS restoration filter is presented. The resulting new CLS filter is intuitive, effective and based on a rigorous derivation. The issue of selecting the user-specified inputs for this new CLS filter is discussed in some detail. In addition, we present simulation-based restoration examples for a FLIR (Forward Looking Infra-Red) imaging system to demonstrate the effectiveness of this new CLS restoration filter.

Paper Details

Date Published: 20 October 1993
PDF: 16 pages
Proc. SPIE 2028, Applications of Digital Image Processing XVI, (20 October 1993); doi: 10.1117/12.158634
Show Author Affiliations
Rajeeb Hazra, NASA Langley Research Ctr. (United States)
Stephen K. Park, College of William and Mary (United States)
G. Louis Smith, NASA Langley Research Ctr. (United States)
Stephen E. Reichenbach, Univ. of Nebraska/Lincoln (United States)

Published in SPIE Proceedings Vol. 2028:
Applications of Digital Image Processing XVI
Andrew G. Tescher, Editor(s)

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