# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 1 Introduction  
Digital cameras and display equipment have a more limited dynamic range than human vision. As a result, when shooting in a high-contrast outdoor scene, oversaturation or undersaturation occur in some areas due to the limited dynamic range. To overcome such a limited dynamic range and create an image similar to human vision, a high-dynamic-range (HDR) image is required. [PERSON] et al. [1] proposed a method for creating HDR images using multiple images with different exposures. This method is used for multiple images with different exposure levels to obtain a camera response function (CRF) and generate an HDR image using them. [PERSON] et al. [2] proposed a method for synthesizing an HDR image using spatial and intensity weighting using two exposure images. These conventional methods require several low-dynamic-range (LDR) images to generate an HDR image, and ghosting artifacts occur when a moving object exists in an image. In addition, cameras capable of shooting HDR images are expensive. Therefore, alternative methods have been proposed for inferring an HDR image from a single LDR image, such as inverse tone mapping (iTM) [3]. Initial iTM methods can improve LDR images, either using individual heuristics or optionally using manual intervention [3, 4, 5]; however, the HDR output is frequently different from actual HDR images because these methods cannot sufficiently compensate for missing information due to sensor saturation, color quantization, and the nonlinearity of CRF [6]. To obtain high-quality HDR images, convolutional neural network(CNN)-based iTM methods have been proposed [6; 7; 8; 9]. CNNs have been used in many computer vision applications; CNN-based iTM methods have demonstrated significant performance improvements. Recently, with state-of-the-art techniques, studies related to HDR image conversion have shown a good performance. [PERSON] et al. [10] developed a fast multiscale structural patch decomposition to demonstrate how multi-exposure can be adjusted to reduce halo artifacts. [PERSON] et al. [11] used CNN learning as a single image contrast enhancement method; the authors constructed a large-scale multi-exposure image dataset containing 589 high-resolution multi-exposure sequences, which were finely selected with 4413 images. They generated contrast enhancement images for each sequence using 13 representative multi-exposure image fusion and stack-based HDR imaging algorithms and conducted subjective experiments to find the best model for each scene. [PERSON] et al. [12] introduced a method for training neural networks using an illumination map in under-exposed photo enhancement using deep illumination estimation. The results of restoring underexposed image pair data showed that image-to-light mapping was learned (instead of image-to-image mapping), leveraging the simplicity of lighting in natural images so that the networks could effectively learn various photo adjustments. To address the problems raised by the preceding cases, we considered the sue of generative adversarial networks (GANs). GANs can generate new images using a competing network of generators and discriminators and have been proven to have the ability to not only increase image resolution but also convert them to new images suitable for use in various fields, such as underwater photo transformation, art, culture, security, and the military [13]. GANs trained for image-to-image translation can map parts of an image to a target image via methods such as texture conversion [14]. When converting an input image to a target image, the expected result image is mapped to the RGB channel of the input image by referring to the target image's pattern. However, it is difficult to obtain sufficient numbers of paired-image datasets using image-to-image conversion. The Berkeley AI Research laboratory's CycleGAN tackled this problem by adding a cycle consistency loss, which feeds back generated images via a reverse function to ensure transitive parity with their source images [15].