# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 3 Proposed Model  
### Motivation  
The conventional technique for creating an HDR image involves shooting two or more pictures with different areas of brightness in the same composition. Thus, every time an HDR image is created, the process must prepare data with different brightness information, which is difficult to accommodate in real-time videos. In addition, deep training models can be easily applied to real-time video after training is complete. Therefore, this study aims to propose a tone compression filter based on GANs to brighten the dark areas of an image and preserve bright areas, regardless of complex processes and data.  
GAN models--deep training models--have proven performance in the image generation field [13; 14; 15; 18; 22; 23; 24; 25]. The proposed model is based on GAN models. It involves gamma shift, RWL, and RFT. GANs require paired training data. Obtaining actual data for GAN training is difficult; however, virtual datasets similar to real datasets that can be used for experiments exist [26].  
Pix2 pix solved the color distortion problem with identity loss, but it is difficult to implement because training and target images must be paired, which requires a lot of data. The proposed model solved the problem of pix2 pix based on CycleGAN.  
The proposed model uses a gamma shift strategy for the L-channel dataset to train numerous tones, effectively reducing the color space distortion introduced by the conventional CycleGAN model. Initially, an experiment was conducted with EVs as an input to the neural network [18]. This model performed well for dark images, but bright images were distorted. Therefore, gamma shift was applied to input images to train wide information.  
Next, the RWL was used to train the dataset's local brightness. This function was modified from EV GAN. In the conventional method, dark images are source images. Therefore, the RWL was implemented to enhance dark images. In the modified method, because an image with a gamma value less than 1 was added, RWL equations for bright and dark images were independently designed. This function primarily trained the brightness of the local area in the target data.  
Finally, an RFT method was used to balance the restoration speed for each local domain. The RFT function was used to adjust the improvement of dark and bright areas to a certain level within the same epoch; it fused the results of the previous and current epochs. This method was advantageous for training the local tone of images.  
First, source images were constructed using gamma shift. Next, each image's weighted loss map was obtained from the luminance channels of the source and target images. The above maps were used by the generator to train the local brightness values of both images. Then, the generator generated images. Finally, the restoration speed of local brightness was adjusted in the RFT step. The RGB test image was separated by luminance and chrominance channels. The luminance channel, to which mean gamma tuning was applied, was used for testing. The chrominance channel was a fusion obtained through training.