TY - JOUR
T1 - Constrained least-squares image restoration filters for sampled image data
AU - Hazra, Rajeeb
AU - Park, Stephen K.
AU - Louis Smith, G.
AU - Reichenbach, Stephen E.
N1 - Funding Information:
This research was undertaken as part of a National Aeronautics & Space Administration contract (NASI 19000) awarded to Lockheed Engineering & Sciences Co. in Hampton, VA. We wish to thank Kurt Severance, NASA Langley Research Center, for providing access to quality image reconstruction hardware and Mousumi Mitra, Lockheed Engineering & Sciences Co. for helpful comments during the preparation of the manuscript.
Publisher Copyright:
© 1993 SPIE. All rights reserved.
PY - 1993/10/20
Y1 - 1993/10/20
N2 - 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 modelbased 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.
AB - 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 modelbased 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.
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U2 - 10.1117/12.158634
DO - 10.1117/12.158634
M3 - Conference article
AN - SCOPUS:0010314434
VL - 2028
SP - 177
EP - 192
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
SN - 0277-786X
T2 - Applications of Digital Image Processing XVI 1993
Y2 - 11 July 1993 through 16 July 1993
ER -