# Image-to-Image Translation Using Identical-Pair Adversarial Networks
## 4 Experiments and Results
### Experimental Results  
Figure 2 displays our visual results of night-to-day, edge-to-photo, aerial-to-map, and de-raining using our model. We use a real raining image to test our model. Even though the raining image in the de-raining dataset was artificially created by the image editor, our approach produced a very nice clear de-raining on the real raining image, as shown in the figure. We compared our results with some existing methods on several tasks depending on the best results for each approach, such as ID-cGANs, pix2 pix, bicycleGANs, PAN, Dual-GAN, and cycleGAN. In Figure 3, we compared our de-raining result with those of ID-cGANs and PAN. Our result looks sharper and clearer than did the results of ID-cGANs and PAN even after zooming in. The result of PAN de-raining looks smoother than ours, but it contains blurriness. Figure 3: Comparison of de-raining tasks with ID\(<\)GAN and PAN on a real rainy image. Figure 2: Sample results. We used our proposed iPANs to solve several tasks of image-to-image translation such as right-to-day, edge-to-photo, map-to-photo, photo-to-map, and de-raining. Figure 4 compares the method with pix2 pix, Dual-GANs, and PAN on the label\(\rightarrow\)facade. The iPANs obtained a better visual facade than pix2 pix but lost to PAN, because PAN produced a more realistic result. However, our results on aerial\(\rightarrow\)map resembles the ground-truth image more than did ID-cGAN and PAN. Visual-quality shows that nighttime to daytime results in Figure 5 look more realistic and brighter, like a daytime image, than did those of other methods. Pix2 pix has a very blurry result, and still contains some darkness. BicycleGANs look realistic daytime but still remain a little bit dark. Figure 4: Comparison of the label\(\rightarrow\)facade with Pix2 pix, Dual-GAN, and PAN. Figure 5: Comparison of night \(\rightarrow\) day with pix2 pix, BicycleGANs. Finally, Figure 6 shows the results of satellite photo to map translation. Pix2 pix and PAN produced curly road map while ours resembles the real sample images. In Table 3, we exhibit our numerical results with existing methods through evaluation metrics. Our method on edge\(\rightarrow\)shoes outperformed Pix2 pix and PAN at PSNR 19.51 and SSIM 0.88 and UQI 0.94, and VIF 0.24. Our de-raining results achieved higher scores at PSNR 31.59, SSIM 0.89, UQI 0.78, and VIF 0.51 than did ID-cGANs or PAN. Our method on aerial\(\rightarrow\)map outperformed pix2 pix and PAN at PSNR 31.9, SSIM 0.79, UQI 0.64 and VIF 0.23. We obtained a higher score than pix2 pix and PAN and Dual-GAN on the label\(\rightarrow\)facade at PSNR 14.08 and UQI 0.86 and VIF 0.14, and lost to Dual-GAN at SSIM 0.53. Our task on the night\(\rightarrow\)day obtained a higher score than pix2 pix and bicycleGANs at PSNR 18.13 and UQI 0.88. Also, bicycleGANs outperformed ours at SSIM 0.64, and Pix2 pix obtained the same result as ours at VIF 0.14. As shown in Figure 1, we used U-NET as our image-transformation network and perceptual similarity network as our discriminator. To address whether our chosen networks are more beneficial, we used ResNet and basic CNNs for discriminator, then compared. In other words, we replaced image generative network (which is ResNet) and discriminator network used in Pix2 pix [35] for our architecture's comparison. This comparison is to clarify why our choice of using U-NET (as an image-transformation network) and perceptual similarity network (as discriminator) is more suitable for our logical proposed approach. Figure 6: Comparison of the photo\(\rightarrow\)map tasks with Pix2 pix, PAN. According to the results shown in Figure 7, iPANs w/ G&DP was not balanced. This case happened to Pix2 pix. We added L1 to our objective function (iPANs w/ G&DP + L1), but the results were blurry. The authors of pix2 pix proposed additional L1 to their objective function and the results were also blurry. Then we replaced L1 with our perceptual loss (iPANs w/ G&DP + P). The results performed much better compared to the previous two models.