Affiliations: Department of Electrical and Computer Engineering,
Tufts University, Medford, MA, USA | American Science and Engineering, Billerica, MA,
USA
Note: [] Corresponding author: Brian H. Tracey, Department of Electrical
and Computer Engineering, Tufts University, Medford, MA 02155, USA.
E-mail:btracey@eecs.tufts.edu
Abstract: While recent years have seen considerable progress in image
denoising, the leading techniques have been developed for digital photographs
or other images that can have very different characteristics than those
encountered in X-ray applications. In particular here we examine X-ray
backscatter (XBS) images collected by airport security systems, where images
are piecewise smooth and edge information is typically more correlated with
objects while texture is dominated by statistical noise in the detected signal.
In this paper, we show how multiple estimates for a denoised XBS image can be
combined using a variational approach, giving a solution that enhances edge
contrast by trading off gradient penalties against data fidelity terms. We
demonstrate the approach by combining several estimates made using the
non-local means (NLM) algorithm, a widely used patch-based denoising method.
The resulting improvements hold the potential for improving automated analysis
of low-SNR X-ray imagery and can be applied in other applications where edge
information is of interest.
Keywords: X-ray backscatter, image denoising, non-local means
