# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 2 Background  
### Inverse Tone Mapping  
iTM is a method that converts a single LDR image into an HDR image. It expands the contrast range of an LDR image using a single LDR image with missing information. Missing information arises due to the limited dynamic range of sensors, under- or over-sensor saturation, the nonlinearity of the CRF, and quantization. Therefore, reconstructing an HDR image from missing information is an ill-posed problem, and it is difficult to obtain a high-quality HDR image. Initial iTM models used the density of a light source to expand dynamic ranges. [PERSON] et al. [3] used a linear interpolation of an original LDR image and an expanded LDR image using an expand map. They used the inversion of the global photographic tone reproduction operator to generate the expanded LDR image. The expand map is a weighting map that estimates the highlight regions and avoids blocky transition between the regions when generating an HDR image. [PERSON] et al. [4] proposed a model that could improve the range of legacy videos and photographs for viewing HDR displays. This model converts a nonlinear LDR image into an HDR image using inverse gamma and contrast scaling. Then, brightness enhancement and an edge-stopping function are used to compensate for the saturated regions. [PERSON] et al. [5] proposed an iTM model based on the human visual system. It adopted the retina response of the human visual system and used an LDR image to achieve local luminance. The LDR image and local luminance were used in the inversed local retina response to reconstruct an HDR image. However, the above iTM models still exhibit the problem of being ill-posed due to insufficient information (data), and the HDR images resulting from these models have limited quality.  
To obtain high-quality HDR images, CNN-based iTM models have been proposed. [PERSON] et al. [7] proposed HDRCNN, which focuses on recovering missing information in overexposed areas using a hybrid dynamic range autoencoder while ignoring underexposed areas. [PERSON] et al. [6] proposed deep reverse tone mapping to mitigate the ill-posed problem that occurs when directly inferring a 32-bit HDR image from an 8-bit LDR image. The core of this model is to composite LDR images shot with different exposures based on supervised learning and merge them to reconstruct an HDR image. A three-dimensional deconvolution network was used to learn relative changes in pixel values according to exposure changes. [PERSON] et al. [8] proposed ExpandNet, which consists of global, local, and dilation branches. The global branch has a pyramid-shaped structure that gradually decreases from the input to output layers to reduce the dimension of an input image and obtain abstract features. The local and dilation branches provide localized processing without down-sampling to capture high-frequency and neighboring features. [PERSON] et al. [9] proposed three specialized CNNs to recover an HDR image from an LDR image: Dequantization-Net for reducing noise and contouring artifacts, Linearization-Net for estimating the CRF and converting nonlinear LDR images into linear irradiance, and Hallucination-Net for recovering missing contents caused by dynamic range clipping.