Share Email Print
cover

Proceedings Paper

Constrained image restoration applied to passive millimeter-wave images
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Passive millimeter-wave imaging has excellent all weather capability but is severely diffraction limited and requires large apertures to give adequate spatial resolution. Linear restoration can enhance the resolution by a factor of two, while under favorable conditions non-linear restoration can enhance it by factors of four. The amount of enhancement possible is generally limited by the amount of noise present in the original observed image. Preprocessing can reduce the effect of this noise. In many non-linear restoration techniques the amount of high spatial frequency introduced into the restored image is uncontrolled. This problem has been overcome through the use of the Lorentzian algorithm, which imposes a statistical constraint on the distribution of gradients within the restored image. Another way of applying a constraint is to selectively restore an image. The high spatial frequency content of an image exists largely at edges and sharp features and needs to be restored, while the smoother background between features contains fewer high frequencies and needs less restoration. Adaptive non-linear restoration techniques have been investigated whereby the amount of restoration applied to an image is a function of the first and second derivative of the image intensity. Images are presented to demonstrate the effectiveness of these methods.

Paper Details

Date Published: 10 November 2004
PDF: 10 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.565103
Show Author Affiliations
Alan H. Lettington, Univ. of Reading (United Kingdom)
Naomi E. Alexander, Univ. of Reading (United Kingdom)


Published in SPIE Proceedings Vol. 5573:
Image and Signal Processing for Remote Sensing X
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top