Optimized maximum-likelihood algorithms for superresolution of passive millimeter-wave imagery

Ho-Yuen Pang; Malur K. Sundareshan; Sengvieng A. Amphay

doi:10.1117/12.319399

21 August 1998 Optimized maximum-likelihood algorithms for superresolution of passive millimeter-wave imagery

Ho-Yuen Pang, Malur K. Sundareshan, Sengvieng A. Amphay

Proceedings Volume 3378, Passive Millimeter-Wave Imaging Technology II; (1998) https://doi.org/10.1117/12.319399
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Ho-Yuen Pang, Malur K. Sundareshan, and Sengvieng A. Amphay "Optimized maximum-likelihood algorithms for superresolution of passive millimeter-wave imagery", Proc. SPIE 3378, Passive Millimeter-Wave Imaging Technology II, (21 August 1998); https://doi.org/10.1117/12.319399

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