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Optical Engineering

Enhanced iterative processing algorithms for restoration and superresolution of tactical sensor imagery
Author(s): Malur K. Sundareshan; Supratik Bhattacharjee
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Paper Abstract

Imagery data acquired in practice to support tactical surveillance and tracking missions in hostile environments typically suffer from a variety of degradations making it essential to subject the data to digital postprocessing aimed at restoration and superresolution before they can be used for any image exploitation tasks (visualization, target detection and characterization, etc.). A number of novel iterative techniques for resolution enhancement are presently being developed, with statistical optimization and set-theoretic estimation offering two popular approaches for algorithm design. The challenges posed by the processing needs of tactical imagery data often require greater capabilities than what the existing algorithms can offer, however, and typically require more enhanced procedures to achieve satisfactory restoration and superresolution. We outline three such enhancements: parallel projection implementation with adaptive relaxation, use of scene-derived information for constraint set design, and a hybrid statistical and set-theoretic estimation procedure. The restoration and superresolution performance of an iterative algorithm that incorporates these enhancements is illustrated by application to tactical imagery data [images acquired from state-of-the-art synthetic aperture radar (SAR) and passive millimeter-wave (PMMW) sensors].

Paper Details

Date Published: 1 January 2004
PDF: 10 pages
Opt. Eng. 43(1) doi: 10.1117/1.1626665
Published in: Optical Engineering Volume 43, Issue 1
Show Author Affiliations
Malur K. Sundareshan, Univ. of Arizona (United States)
Supratik Bhattacharjee, Univ. of Arizona (United States)

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