Measuring and correcting wobble in large-scale transmission radiography

TL;DR

This paper presents a comprehensive approach to measure and correct wobble in large-scale transmission radiography, significantly improving image precision and threat detection capabilities.

Contribution

It introduces a new model, calibration techniques, and a Random Regression Forest method for real-time wobble estimation and correction in radiography systems.

Findings

Corrects 87% of image error caused by wobble

Significant improvement in image quality for difficult scenes

Enhanced threat detection potential

Abstract

Large-scale transmission radiography scanners are used to image vehicles and cargo containers. Acquired images are inspected for threats by a human operator or a computer algorithm. To make accurate detections, it is important that image values are precise. However, due to the scale of such systems, they can be mechanically unstable, causing the imaging array to wobble during a scan. This leads to an effective loss of precision in the captured image. We consider the measurement of wobble and amelioration of the consequent loss of image precision. Following our previous work, we use Beam Position Detectors (BPDs) to measure the cross-sectional profile of the X-ray beam, allowing for estimation, and thus correction of wobble. We propose: (i) a model of image formation with a wobbling detector array; (ii) a method of wobble correction derived from this model; (iii) methods for calibrating sensor sensitivities and relative offsets; (iv) a Random Regression Forest based method for instantaneous estimation of detector wobble, and (v) using these estimates to apply corrections to captured images of difficult scenes. We show that these methods are able to correct for $87\%$ of image error due wobble, and when applied to difficult images, a significant visible improvement in the intensity-windowed image quality is observed. The method improves the precision of wobble affected images, which should help improve detection of threats and the identification of different materials in the image.