## 4 Methodology  
To address the challenge of generating high-quality X-ray images of prohibited items, we explored image augmentation using generative adversarial networks (GANs) with Pix2 Pix and Pix2 PixHD as our core frameworks. We implemented a novel three-channel input technique to augment the model's ability to produce high-quality X-ray images. The first channel was dedicated to masks, assigning specific pixel values to distinctly identify object categories. The second and third channels incorporated edge data: Edge01, extracted from the shape of the object, and Edge02, generated through automated edge detection techniques on ground-truth images. The integration of Edge1 and Edge2, known as \"Hybrid Edge\", was designed to overcome the limitations of single-channel inputs, which often lacked detail and texture. The combination of mask data with hybrid edges significantly improved the model's capability to generate images that were both visually accurate and detailed. The mask channel enabled precise object recognition and categorization, while the inclusion of both manually annotated and computationally derived edges allowed the model to depict a broad range of details, from sharp object outlines to subtle textural nuances essential for realistic X-ray imaging. Regarding the edge types employed:  
**Edge01:** the shapes of objects were directly extracted from X-ray images, with a focus on capturing geometric outlines based on the annotations provided in the dataset [45]. **Edge02:** this involved converting images to greyscale, applying Gaussian blur to reduce noise, and using the Canny edge detection algorithm to enhance the detection of finer details and textures. **Hybrid Edge:** A method that combined Edge01 and Edge02 to enhance image input data, improving both texture detail and shape accuracy. This approach overcame the limitations of Edge01's focus on outlines and Edge02's focus on internal details, providing a comprehensive solution for generating detailed and accurate images. This approach is illustrated in Figure 2.  
These techniques collectively enable the model to simulate realistic and detailed X-ray images of prohibited items, highlighting the advantages of integrating diverse technological approaches in image synthesis. To achieve this high level of detail and realism, our methodology primarily utilised the Pix2 Pix and Pix2 PixHD models, which were pivotal in addressing the high resolution and detail-oriented requirements of our research. We chose  
Figure 2: Examples of images used in our experiment (**A**) real images, (**B**) edges from mask, (**C**) edges from automated method, and (**D**) hybrid edges. Pix2 Pix and Pix2 PixHD because of their exceptional capabilities in generating detailed, realistic images from paired datasets, whereas other models like CycleGAN, while adept at unpaired image translation, lacked the precision needed for X-ray security imaging due to their insufficient detail preservation [36]. Similarly, super-resolution techniques like SRGAN, although useful for enhancing resolution, do not facilitate the essential semantic-to-photo translation required. StyleGAN models, focusing primarily on artistic effects, did not meet the accurate semantic correspondences crucial for security applications. Also, DCGAN, known for its efficiency in generating quality images from noise, was unsuitable due to its inability to perform paired image translation [37], a key requirement for achieving the high fidelity necessary in our work. In contrast, Pix2 Pix and Pix2 PixHD, with their robust methodologies for handling paired data, provided a superior solution that aligned perfectly with the stringent needs of creating high-quality X-ray security images, ensuring both detail accuracy and high resolution. **Pix2 Pix model:** Introduced by [PERSON] [46] in 2016, it is a significant image-to-image translation framework using a conditional generative adversarial network (cGAN) architecture designed to learn mappings from input to output images using paired data sets from two distinct domains.