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Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery

Isaac-Medina, B.K.S. and Bhowmik, N. and Willcocks, C.G. and Breckon, T.P. (2022) 'Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery.', Proc. Computer Vision and Pattern Recognition Workshops New Orleans, Louisiana, 19-24 June 2022.


Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of the material. Recently, paired image to image translation models based on conditional Generative Adversarial Networks (cGAN) have shown to be effective for image colourisation. In this work, we investigate the use of such a model to translate from the raw X-ray energy responses (high, low, effective-Z) to the pseudo-coloured images and vice versa. Specifically, given N X-ray modalities, we train a cGAN conditioned in N − m domains to generate the remaining m representation. Our method achieves a mean squared error (MSE) of 16.5 and a structural similarity index (SSIM) of 0.9815 when using the raw modalities to generate the pseudo-colour representation. Additionally, raw X-ray high energy, low energy and effective-Z projections were generated given the pseudo-colour image with minimum MSE of 2.57, 5.63 and 1.43, and maximum SSIM of 0.9953, 0.9901 and 0.9921. Furthermore, we assess the quality of our synthesised pseudo-colour reconstructions by measuring the performance of two object detection models originally trained on real X-ray pseudo-colour images over our generated pseudo-colour images. Interestingly, our generated pseudo-colour images obtain marginally improved detection performance than the corresponding real X-ray pseudo-colour images, showing that meaningful representations are synthesized and that these reconstructions are applicable for differing aviation security tasks.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:11 April 2022
Date deposited:05 May 2022
Date of first online publication:18 June 2022
Date first made open access:25 June 2022

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