# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 4 Experiment Results
### Comparisons with Conventional Methods
Figure 13 shows an image of dark surroundings on the inside of a building, and the goal of each model is to maintain the brightness of the bright ceiling and windows. In Figure 13c, the color of the dark area has become gray. There is little change in Figure 13d. Figure 13e is relatively bright, but there is no change in the dark area in the upper right corner of the image. Figure 13f is over brightened. Figure 13g features color distortion. In Figure 13h, the dark areas are brightened while maintaining the brightness of the windows and ceiling. Figure 14 shows a dark surrounding image of the inside of the building. The image has a chair and a stained glass. The goal of each model is to restore the brightness of the chairs, stained glass, and the inner walls of the building. In Figure 14c, the color of the stained glass becomes red. There is little brightness change in Figure 14d. Figure 14e shows the chair, but the left and right sides of the image are still dark. In Figure 14f, the brightness is increased, but the noise in the dark area is also increased. The brightness in Figure 14g is unchanged. In Figure 14h, the image generated by the proposed method shows that the chair is identifiable and the structure of the inner wall of the building is observable. Figure 15 show an outdoor image of bright surroundings. The image shows a young girl and her reflection in the car's window and the background. The goal of each model is to brighten the shadows of Figure 15b and the girl's face, restore the reflection in the window, and maintain the brightness of the background. Figure 15c shows the color distortion of the vehicle and the girl's face turns gray. There is little change in Figure 15d. Figure 15e shows an oversaturated background. Figure 15f shows a low contrast and color distortion. Figure 15g is darker than the input image. In Figure 15h, the proposed method preserves the color of the input image well and has a high contrast. Figure 16 is an image of bright surroundings. The goal of each model is to restore detail in the trees and restore the brightness inside the white pillars of the building and the ground. Figure 16c shows the color distortion of the pillars of the building. Most of the tree detail information is lost. There is no improvement in brightness in Figure 16d. Figure 16e is similar to the ground truth, but some noise is observed. Figure 16f is saturated. Figure 16g is darker than the input image. In Figure 16h, the proposed method has less color distortion and noise and a better local contrast than the image produced using conventional methods. Unlike Lum CycleGAN, EV GAN, and the proposed models, it seemed that CycleGAN, which learned RGB, trains all channels, even with the same number of training epochs, resulting in underfitting. In Lum CycleGAN, the color distortion problem is resolved and fog images are resolved to some extent, but no learning of local tone mapping is performed, indicating that the training network did not learn about chroma and required more training. The reason for this problem was that EV GAN, similar to the proposed model, also proceeded with learning for local tone mapping but used a limited set of training datasets, which illuminated dark images. Meanwhile, in the proposed model, we use the RWL and RFT of EV GAN differently depending on the gamma value of the training image to perform local tone mapping with a wide range of luminance. Compared with state-of-the-art technology, the results of ExpandNet are barely changed. The overall results are noisy and have a low contrast. The results of HDRCNN show very low saturation and contrast. To compare the quantitative performance results, we used four evaluation methods--namely, a perception-based image quality evaluator (PIQE) [29] multiscale structural similarity (MS-SSIM) [30], HDR-VDP2 quality, and HDR-VDP2 visibility [31]. The PIQE was computationally less efficient but provided a global quality score and local quality measure.