## 1 Introduction
Other studies have evaluated the performance of Fast RCNN and single-shot multibox detector (SSD) [25] in both single and multiview X-ray imagery. Additionally, transfer learning approaches [1] have been explored to see if algorithms can generalize across different types of scanners in a multi-class detection problem [26; 27; 28]. Image synthesis in deep learning involves creating new synthetic images using techniques such as generative adversarial networks (GANs) or variational autoencoders [29; 30; 31]. This method is critical in fields such as medical imaging and security, where diverse and comprehensive datasets are required to train effective models. In medical X-rays, image synthesis enables researchers to expand training datasets and address the challenge posed by the limited availability of real patient data, which is often restricted due to privacy and security regulations. This expansion helps improve the models' ability to identify subtle disease indicators that are not well represented in available datasets, enhancing the performance of diagnostic tools. In security applications, especially X-ray screening, synthetic images play a critical role in enhancing the effectiveness of detection systems. The availability of X-ray datasets for security purposes is typically limited due to strict privacy and security regulations. This restricts the publication and use of real X-ray images. Researchers can simulate a wide range of security scenarios and objects, such as hidden weapons or other contraband, by generating synthetic X-ray images. This allows for the training of more robust models capable of identifying potential threats more accurately. Enhanced training through synthetic imagery not only improves the detection capabilities of security systems but also enhances their reliability, ensuring they perform effectively under diverse and challenging conditions. Our research focuses on the application of deep learning techniques to the field of X-ray image synthesis, particularly using the Pix2 Pix and Pix2 PixHD models. We explore these models for their ability to generate synthetic X-ray images, a crucial capability for expanding the available data in security imaging. Unlike traditional X-ray imaging, which relies on a direct analysis of captured images, our approach involves creating new imagesthat are similar to real X-ray scans. The limited availability of X-ray data, often restricted due to privacy concerns in this field, presents challenges that this method addresses. Deep learning has also enhanced the accuracy of X-ray security imaging techniques. The classification of X-ray security imaging methods includes classical image analysis, image enhancement, threat image projection, and deep learning [32; 33]. In this study, we use a dataset that includes a wide range of hazardous products sorted into 35 distinct categories. This dataset was created using advanced X-ray scanning technology. Through a series of carefully designed experiments, we evaluate the ability of these models to transform mask and edge data into detailed X-ray images. We compare their performance and highlight notable enhancements in image quality and realism, demonstrating the potential of deep learning to improve the accuracy of X-ray security imaging techniques. Our study provides a comprehensive overview of our methodology, from dataset preparation and model configuration to the experimental setups and evaluation criteria used to assess performance. By presenting a comparative analysis of Pix2 Pix and Pix2 PixHD in the context of X-ray image synthesis, we aim to contribute valuable insights to the ongoing discourse in image-to-image translation research and its practical applications, setting the stage for future advancements in this promising field. The contributions of this study can be summarized as follows:  
* We introduce a novel hybrid edge with a mask input method for enhancing the quality of generated X-ray images.