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Proceedings Paper

Applications of pointed ultra-resolution method in microwave imaging
Author(s): Evgeni Nikolaevich Terentiev; Nikolay E. Terentiev
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Paper Abstract

The problem of compensation for various distortions is solved with the aid of diminishing of PSF O distortions. The laws of distortions are presented as scalar product I(y)=(O, Ix), Ix is the undistorted image, which is localized as PSF O, I(y) is a value of the image Iy in the point x=y. The pointed algorithm of compensation for PSF O distortions is as follows. The scalar products I(z)=(R, Iy) are calculated in analogous way for all the points x=z, Ix ~ Iz, Iz is a image with the compensated distortions. The small dimensions variation problem for resolving function R is a well definite one. It is necessary to calculate all the different resolving functions Rx, if the distorted image Iy has the domains with the different distortions because of the different PSF Ox. That means, we may successfully use a pointed ultra-resolution method in all the modern multi-sensors (digital cameras) or multi-rays (microwave imaging) receiving systems. For the compensation of distortions we do not solve Fredholm equation of the first kind, our small dimension solutions for R cannot, in principle, contain the high frequency spatial oscillations. We emphasize that these high frequency oscillations in operator analogue of R are the main obstacle of the compensational methods based on solution of Fredholm equation of the first kind. Examples of applying of the pointed ultra-resolution method in microwave imaging are considered.

Paper Details

Date Published: 19 May 2005
PDF: 11 pages
Proc. SPIE 5789, Passive Millimeter-Wave Imaging Technology VIII, (19 May 2005); doi: 10.1117/12.603569
Show Author Affiliations
Evgeni Nikolaevich Terentiev, M.V. Lomonosov Moscow State Univ. (Russia)
Nikolay E. Terentiev, M.V. Lomonosov Moscow State Univ. (Russia)

Published in SPIE Proceedings Vol. 5789:
Passive Millimeter-Wave Imaging Technology VIII
Roger Appleby; David A. Wikner, Editor(s)

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