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29 July 2002 New iterative processing algorithms for restoration and super-resolution of tactical sensor imagery
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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 post-processing aimed at restoration and super-resolution 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 and super- resolution are presently being developed. In this paper, we shall outline two popular avenues that have been followed for constructing iterative processing algorithms, viz. statistical optimization and set-theoretic estimation, and discuss the super-resolution abilities of a powerful algorithm developed from a hybridization of the two approaches. Performance of this algorithm in enhancing resolution in tactical imagery data is illustrated. Notwithstanding the significant resolution enhancement that is possible with these algorithms, the spectrum extrapolation that is central to super-resolution comes only as a by-product and needs to be checked only after completion of the processing steps in order to ensure that an expansion of the image bandwidth has indeed occurred. To overcome this limitation, a new approach of mathematically extrapolating the image spectrum and using it as a constraint in a Projection-Onto-Convex-Sets (POCS) estimation framework to construct an iterative processing algorithm with guaranteed levels of super-resolution is presented.
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Malur K. Sundareshan and Supratik Bhattacharjee "New iterative processing algorithms for restoration and super-resolution of tactical sensor imagery", Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, (29 July 2002);

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