Share Email Print

Proceedings Paper

The application of wavelet denoising in material discrimination system
Author(s): Kenneth Fu; Dale Ranta; Clark Guest; Pankaj Das
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Recently, the need for cargo inspection imaging systems to provide a material discrimination function has become desirable. This is done by scanning the cargo container with x-rays at two different energy levels. The ratio of attenuations of the two energy scans can provide information on the composition of the material. However, with the statistical error from noise, the accuracy of such systems can be low. Because the moving source emits two energies of x-rays alternately, images from the two scans will not be identical. That means edges of objects in the two images are not perfectly aligned. Moreover, digitization creates blurry-edge artifacts. Different energy x-rays produce different edge spread functions. Those combined effects contribute to a source of false classification namely, the "edge effect." Other types of false classification are caused by noise, mainly Poisson noise associated with photons. The Poisson noise in xray images can be dealt with using either a Wiener filter or a wavelet shrinkage denoising approach. In this paper, we propose a method that uses the wavelet shrinkage denoising approach to enhance the performance of the material identification system. Test results show that this wavelet-based approach has improved performance in object detection and eliminating false positives due to the edge effects.

Paper Details

Date Published: 28 January 2010
PDF: 12 pages
Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 75380Z (28 January 2010); doi: 10.1117/12.838648
Show Author Affiliations
Kenneth Fu, Univ. of California, San Diego (United States)
Dale Ranta, SAIC (United States)
Clark Guest, Univ. of California, San Diego (United States)
Pankaj Das, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 7538:
Image Processing: Machine Vision Applications III
David Fofi; Kurt S. Niel, Editor(s)

© SPIE. Terms of Use
Back to Top