# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 4 Experiment Results
### Comparisons with Conventional Methods  
We compared the results of the proposed method with those of five conventional methods, CycleGAN, Lum CycleGAN, EV GAN, HDRCNN, and ExpandNet. Figures 9 and 10 are foggy images and Figures 11 and 12 are high-contrast images. Figures 13 and 14 are the dark surrounding images inside the building and Figures 15 and 16 are the bright surrounding images outdoors. Figure 8: Comparison of general brightness augmentation and the proposed model’s gamma shift augmentation: (**a**) general brightness augmentation, (**b**) gamma shift augmentation, and (**c**) ground truth. Figure 10: Foggy image #2: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 9: Foggy image #1: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 11: High-contrast image #1: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 12: High-contrast image #2: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 14: Dark surrounding image #2: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 13: Dark surrounding image #1: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 9 is a satellite image of the sea and continent. In Figure 9c, CycleGAN shows color distortion and noise in the whole image. In Figure 9d, Lum CycleGAN shows that the sea and continent are darkened. In Figure 9e,f, EV GAN and HDRCNN show low contrast and desaturation, which makes it difficult to identify objects. In Figure 9g, ExpandNet shows the blocking noise. In the proposed method shown in Figure 9h, the fog is removed and the contrast is higher than that seen in other methods. The result is also similar to the target image of Figure 9b. Figure 10 is a satellite image of the port. The result of CycleGAN in Figure 10c shows that the sea and port colors are distorted to red and noise appears around the port. In Lum CycleGAN of Figure 10d, it is mostly dark except for the port. The results of EV GAN and HDRCNN show a very low contrast in Figure 10e,f. The result of ExpandNet in Figure 10g is so dark that most of the information is lost. The proposed method shown in Figure 10h is restored so that ports and seas are well identified. Figures 11 and 12 are high-contrast images. In Figure 11c, the color of the illuminated area is distorted. There is no change in Figure 11d. Figure 11e is brightened overall, but the  
Figure 16: Bright surrounding image #2: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. Figure 15: Bright surrounding image #1: (**a**) input, (**b**) target, (**c**) CycleGAN, (**d**) Lum CycleGAN, (**e**) EV GAN, (**f**) HDRCNN, (**g**) ExpandNet, and (**h**) the proposed model. clothes of the people are difficult to identify. Figure 11f has a low saturation. Figure 11g shows that the people in the shadows have lost their information. Figure 11h shows that all people and backgrounds are identifiable. In Figure 12c,d, the brightness is not changed. Additionally, the color of outside the window is distorted in Figure 12c. Figure 12e is brighter than the input image, but objects behind the person are dark. Figure 12f shows a low contrast. In Figure 12g, there is no improvement in brightness. In Figure 12h, the result of proposed method well enhances the brightness and contrast.