Review of super-resolution techniques for passive millimeter-wave imaging

Alan H. Lettington; Marc R. Yallop; Dennis Dunn

doi:10.1117/12.477467

29 July 2002 Review of super-resolution techniques for passive millimeter-wave imaging

Alan H. Lettington, Marc R. Yallop, Dennis Dunn

Proceedings Volume 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing; (2002) https://doi.org/10.1117/12.477467
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

This paper reviews the mathematical image processing methods available for super-resolving passive millimeter wave (PMMW) images. PMMW imaging has a number of advantages over infra- red (IR) and visible imaging in being able to operate under adverse weather conditions making it useful for all weather surveillance. The main disadvantage, however, is the size of aperture required to obtain usable spatial resolution. A typical aperture size would be 1 m diameter for a system operating at 94GHz. This aperture may be reduced if super- resolution techniques are employed. To achieve super- resolution non-linear methods of restoration are required in order to generate missing high frequency information. For thee to be genuine high frequencies it is necessary to restore the image subject to constraints. These constraints should apply directly to the scene content rather than to properties of any noise also present. The merits of the available super-resolution techniques are discussed with reference to sharpening noisy PMMW images. Any increase in sharpness of an image frequently results in an increase in the noise present. This can detract from the ability of a human observer to recognize an object in the scene. This problem is discussed with reference to a recent model of human perception.

Citation Download Citation

Alan H. Lettington, Marc R. Yallop, and Dennis Dunn "Review of super-resolution techniques for passive millimeter-wave imaging", Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, (29 July 2002); https://doi.org/10.1117/12.477467

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