## 5 Experiments and Results
### Detailed Experimentation and Results Analysis with Pix2 PixHD
#### 5.4.1 Pix2 PixHD with Different Types of Edge Inputs  
In this experiment, we used edges instead of masks as inputs to our model. We utilised three distinct types of edges to conduct a comprehensive analysis. The effectiveness of different edge inputs for our model was quantitatively evaluated using a comprehensive set of metrics: the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS), Frechet Inception Distance (FID), and Feature Similarity Index Metric (FSIM) scores. These scores provided clear evidence of the varied performance across the methods implemented, as summarized in Table 2.  
Specifically, the method utilising edges derived from X-ray images, denoted as Edge01, achieved a PSNR of 15.76, SSIM of 0.48, LPIPS of 0.40, FID score of 123.45, and FSIM score of 0.39 reflecting its capability but also highlighting areas for improvement. In contrast, Edge02 resulted in a higher PSNR of 17.62, SSIM of 0.56, LPIPS of 0.31, and a lower FID score of 78.48, and an FSIM score of 0.46, indicating better performance in image synthesis quality. Significantly, the combined approach, labelled Hybrid Edge, not only achieved an improved FID score of 71.837 and FSIM score of 0.47 but also showcased a PSNR of \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **PSNR** & **SSIM** & **LPIPS** & **FID Score** & **FSIM Score** \\ \hline Edge01 & 15.76 & 0.48 & 0.40 & 123.45 & 0.39 \\ Edge02 & 17.62 & 0.56 & 0.31 & 78.48 & 0.46 \\ Hybrid Edge & 17.53 & 0.57 & 0.31 & 71.83 & 0.47 \\ Mask–Pix2 Pix & 11.50 & 0.41 & 0.50 & 166.44 & 0.35 \\ Mask–Pix2 PixHD & 16.43 & 0.60 & 0.19 & 35.36 & 0.46 \\ Pix2 Pix-edge and mask & 13.89 & 0.54 & 0.35 & 146.20 & 0.42 \\ Pix2 PixHD-edge and mask & **19.93** & **0.71** & **0.12** & **29.36** & **0.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative impact analysis: effects of diverse inputs on synthetic X-ray image generation with Pix2 Pix and Pix2 PixHD models. 17.53 and an SSIM of 0.57, with an LPIPS of 0.31. This progress highlighted the potential of combining various edge approaches to improve the accuracy of X-ray image generation. Figure 7 presents a visual comparison of the same object-generated image using the three distinct edge input methods. The first image, generated with Edge01, illustrates that while the model was able to capture the overall shape of the object effectively, it fell short in rendering the complex details accurately. This is an indication of the method's ability to grasp general forms, but it has limitations in conveying the finer aspects of texture and small details within the X-ray images. With Edge01, the results indicate that while the shape of objects was well-represented, the details within these objects were not captured effectively. This limitation arises because Edge01 focuses on the outline of objects, omitting finer details and textures inside the object's boundary. For example, in the case of the axe, the input successfully defined the axe's shape, but it did not capture the detailed distinction between different materials (wooden handle versus plastic component) in the handle of the axe. Also, we can notice that the head of the axe is not perfect and a little blurry, as shown in Edge01 in Figure 7. These results are due to the lack of internal detail information in the input, which causes Edge01 to not provide the details of the texture of the object like Edge02 and Hybrid Edge do. Edge02 is designed to capture more of the internal details of objects compared to Edge01 by focusing on the texture and minor contrasts within the X-ray images, as well as the different object components. As a result, Edge02 provided a significant improvement in generating the internal details of the axe's handle, differentiating areas of coloured green and orange (wooden handle versus plastic component) in the handle, with a good generation of the axe's head compared to Edge01.