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Proceedings Paper

Optimized maximum-likelihood algorithms for superresolution of passive millimeter-wave imagery
Author(s): Ho-Yuen Pang; Malur K. Sundareshan; Sengvieng A. Amphay
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Paper Abstract

Iterative image restoration algorithms developed using a maximum likelihood (ML) estimation framework are attaining considerable significance in recent times for super-resolution processing of passive millimeter wave (PMMW) images. In this paper we offer a processor requirements analysis for implementing these algorithms, which provides assurance on the feasibility of their implementation using commercially available microprocessors, even for applications where processing time may be of critical importance. Two optimized versions of these algorithms, one developed by augmenting each iterative estimation step with a post-filtering operation and the other developed by incorporating a background-detail separation approach in the estimation process, are developed which provide superior resolution enhancement performance while simultaneously suppressing noise-induced and ringing artifacts in the restored images. Results of processing data acquired from a 95 GHz 1 foot diameter aperture radiometer are included to demonstrate that these algorithms offer significant superresolution capabilities for processing PMMW imagery.

Paper Details

Date Published: 21 August 1998
PDF: 13 pages
Proc. SPIE 3378, Passive Millimeter-Wave Imaging Technology II, (21 August 1998); doi: 10.1117/12.319399
Show Author Affiliations
Ho-Yuen Pang, Univ. of Arizona (United States)
Malur K. Sundareshan, Univ. of Arizona (United States)
Sengvieng A. Amphay, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3378:
Passive Millimeter-Wave Imaging Technology II
Roger M. Smith, Editor(s)

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