# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 2 Related Works
### Learning Methods  
In recent years, methods utilizing deep learning from input to targeting ground truth have been actively presented, due to the excellent performance on analytic training. Most of them were based on a convolutional neural network (CNN). [PERSON] et al. [7] expanded an 8-bit LDR image to a 16-bit image by utilizing a U-net-based network with a spatial feature transform (SFT) layer. [PERSON] et al. [8] also used a U-net with gamma corrected skip connections. They proposed a masking method to find overexposed regions in images. The masks were created by the maximum value among red, green, and blue channels and used for blending an input image and a predicted image. [PERSON] et al. [9] attempted to capture different levels of features in input LDR images. They proposed 3 different branches--local, dilated, and global branches. Local/global branches have a small/big receptive field for high-level features/low-level features. The dilation branch captures mid-level features. [PERSON] et al. [23] introduced a U-net-based image correction method which reconstructs an HDR image from an LDR image first and then renders a color-corrected LDR image from the HDR domain. [PERSON] et al. [24] suggested an end-to-end feedback network that was effective for rendering a coarse-to-fine representation. They learned the feedback network using the recurrent neural network multiple times resulting in a deeper network for better reconstruction quality. [PERSON] et al. [10] designed a pipeline in a sequence of clipping, a non-linear mapping, and quantization for HDR images to LDR images. They then trained the CNN model to learn each reversed step of the pipeline. For loss functions, MAE, MSE, and perceptual loss from the VGG network [25] were employed. Additionally, [PERSON] et al. [26] utilized perceptual loss. They applied the sum of MAE loss and perceptual loss as a loss function. They used a U-net architecture with masks kept updated every step. [PERSON] et al. [27] suggested the attention approach. They devised a network with two different branches, which are the attention branch and the dilation branch, for finding interdependencies between channels.  
After generative adversarial networks (GAN) [28] were presented, GAN demonstrated its exceptional performance in image translation. Researchers applied the concept of GAN to inverse tone mapping since it also maps input images to target images. A GAN-based inverse tone mapping approach was first proposed by [PERSON] et al. [11]. They suggested the model with adversarial learning to eliminate the misalignment from large motions. [PERSON] et al. [16] proposed a hybrid loss function. The proposed loss function was the sum of reconstruction loss (MAE), adversarial loss (GAN loss), and perceptual loss (VGG loss). [PERSON] et al. [29] also applied the GAN-based framework to their three subnetworks for image reconstruction, a detail restoration, and a local contrast enhancement. [PERSON] et al. [30] used a GAN-based architecture with a Gaussian distribution to transform the density of HDR pixels.  
There has been some research that attempted to use the HSV color space. [PERSON] et al. [12] applied a Hue loss function with a simple MAE. They proposed a network with two branches for overexposed and underexposed areas, respectively. Two kinds of masks, light and gray masks, were employed for training, which was calculated by the gray scale of an LDR image. [PERSON] et al. [13] also employed an HSV loss function. They utilized saturation information for their loss function with a simple MAE. Their network predicted different exposure images from an input image and then merged all of them into an HDR image.