# Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
## 4 Discussion  
Figure 5 displays synthetic traffic sign images generated by DCGAN, LSGAN, and WGAN with epoch 2000 and size 32 \(\times\) 32. Figure 6 shows the realistic synthetic image generated by DCGAN, LSGAN, and WGAN for all classes with 2000 epoch and size 64 \(\times\) 64. Figures 7 and 8 describe the synthetic image generation result using 1000 epoch and size 32 \(\times\) 32 and 64 \(\times\) 64, respectively. Moreover, the image is relatively real because we cannot distinguish which image is fake and which is actual. The images seem very sharp, natural and realistic. Hence, the worst generate images occur while using 50 input images and 1000 epochs, as seen in Figures 7 and 8. The image appears blurry, not clear, and has much noise compared to others. Our experiments empirically tested the data generation by various GANs by calculating the similarity between the synthesized images and their corresponding real images. We measured SSIM values between generated images and authentic images of a similar nature. SSIM includes masking of the luminosity and contrast. The error calculation also involves strong interconnections of closer pixels, and the metric is based on small image windows. Figure 9 describes some examples of the SSIM and MSE calculation for original image and synthetic image by LSGAN. All original image in Figure 9 indicates the same MSE = 0 and SSI = 1. We calculated the SSIM and MSE values for each synthetic image and compared them with the original image. We do this to evaluate which GAN model is the best. Hence, Figure 9 shows MSE = 2.11 and SSIM = 0.81 for class T1. Figure 5: Synthetic traffic sign images of all classes with epoch 2000 and size 32 \(\times\) 32 generated by (a) DCGAN, (b) LSGAN, and (c) WGAN. Figure 6: Synthetic traffic sign images of all classes with epoch 2000 and size 64 \(\times\) 64 generated by (a) DCGAN, (b) LSGAN, and (c) WGAN. Figure 8: Synthetic traffic sign images of all classes with epoch 1000 and size 64 \(\times\) 64 generated by (**a**) DCGAN, (**b**) LSGAN, and (**c**) WGAN. Figure 7: Synthetic traffic sign images of all classes with epoch 1000 and size 32 \(\times\) 32 generated by (**a**) DCGAN, (**b**) LSGAN, and (**c**) WGAN. Figure 9: Structural Similarity Index (SSIM) and Mean Squared Error (MSE) Calculation. (**a**). Class T1 Original Image, (**b**) Class T1 Synthetic Image, (**c**). Class T2 Original Image, (**d**) Class T2 Synthetic Image, (**e**). Class T3 Original Image, (**f**) Class T3 Synthetic Image, (**g**). Class T4 Original Image, and (**h**) Class T4 Synthetic Image. The detailed performance evaluation of synthetic images by various GANs using 1000 and 2000 epochs is presented in Table 4.  
Table 4 represents the complete SSIM and MSE calculation for various GANs. We calculate the average SSIM and MSE for each model, including DCGAN, LSGAN, and WGAN. Moreover, Group 2 analysis using 200 total images as input and size 32 \(\times\) 32 is as follows: LSGAN exhibits the maximum SSIM values at 0.473 and minimum MSE value 4.851 using 1000 epoch. WGAN achieved the second highest with SSIM and MSE scores of 0.452 and 4.963, respectively. DCGAN obtains the minimum SSIM values at 0.315 and maximum MSE value at 6.912 with the same setting. Similarly, using 2000 epoch LSGAN obtains the optimum SSIM values at 0.498, followed by WGAN at 0.482 and DCGAN at 0.468. In contrast, Group 5 obtained the worst experimental results by entering 50 images and dimensions of 64 \(\times\) 64.