## 6 Conclusions and Future Work  
In conclusion, our study showcased the remarkable capabilities of advanced generative models, particularly, Pix2 Pix and Pix2 PixHD, in synthesising realistic X-ray images for security screening applications. By augmenting the dataset with innovative techniques such as mask and hybrid edge inputs, we significantly improved image synthesis fidelity and accuracy. Our experiments conducted on a comprehensive Korean dataset demonstrated the superior performance of our novel mask and hybrid inputs, especially when using the Pix2 PixHD model. The empirical evaluation confirmed the effectiveness of these generative models in overcoming the challenges of data scarcity in X-ray image synthesis. The metrics of PSNR, SSIM, LPIPS, and FID scores that we used in our quantitative analysis attested to the superiority of our proposed techniques. Furthermore, our research stood out by achieving the best FID score compared to other studies in the field, highlighting the quality and realism of the synthesised X-ray images. These findings confirm the potential of generative models for advancing X-ray screening technologies and open avenues for future research to further refine and expand these systems. In future studies, we aim  
Figure 10: Result of utilising mask and hybrid edge as three-channel inputs with the axe from (Figure 9). \begin{table}
\begin{tabular}{l c c} \hline \hline  & **FID** & **Number of Categories** \\ \hline [PERSON] [35] & 111.33 & 3 \\ [PERSON] [2] & 95.20 & 5 \\ [PERSON] [34] & 69.50 & 2 \\ [PERSON] [37] & 37.04 & 9 \\ [PERSON] [36] & 69.50 & 2 \\ Our method & **29.36** & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of FID scores between our method and current state-of-the-art methods. to convert camera images into X-ray images using advanced deep learning techniques. This approach will enable the generation of synthetic X-ray images from available camera images, expanding the variety and volume of training data for security screening models. Such advancements can significantly enhance the accuracy and robustness of detection systems. Additionally, we will focus on creating paired datasets comprising real and synthetic X-ray images, which are crucial for training and validating sophisticated deep learning models. These datasets will be pivotal in various fields, including security, medical imaging, and industrial inspection, aiming to train models that are more accurate and efficient. Moreover, a significant aspect of our future research will focus on enhancing the process of data layer separation in multi-object X-ray images within the context of X-ray security. By leveraging the high-quality synthetic X-ray images and their corresponding ground truths generated through our advanced modelling techniques, we plan to develop innovative methods for effectively distinguishing and isolating overlapping objects within complex scans. This capability will be particularly useful when creating complex images featuring multiple objects in X-ray security settings, each with its own isolated ground truth. We also aim to expand the creation of complex multi-object datasets for X-ray security, which will enhance the application and utility of X-ray images. Conceptualization, B.Y. and M.S.K. ; Methodology, B.Y. and M.S.K. ; Software, B.Y. ; Validation, B.Y. and M.S.K. ; Formal analysis, B.Y. and H.-S.K. ; Resources, H.-S.K. ; Data curation, B.Y. ; Writing - original draft, B.Y. ; Writing - review & editing, B.Y. and M.S.K. ; Visualization, B.Y. ; Supervision, H.-S.K. ; Project administration, H.-S.K. ; Funding acquisition, H.-S.K. All authors have read and agreed to the published version of the manuscript.