# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 1 Introduction  
High dynamic range (HDR) photographs include rich color details by using higher color depths to encode a broad range of real-world luminance. Conventional color images and displays using the 8-bit depth for each color channel can only convey a limited range of color, which is called a low dynamic range (LDR), compared to the HDR. Especially when we take a picture in extremely bright scenery with some dark areas such as shadows under the sunlight or dark scenery with some bright areas such as the moon at night, the picture may partially contain very bright or dark areas. This results in missing details in over/underexposed areas of LDR images. However, creating a sound-quality HDR image with a standard camera setting is bothersome. We usually use exposure bracketing, which shoots multiple exposure images and merges them into an HDR image. There has been active research towards developing the reconstructing process of HDR images from LDR images to be more universal. Thanks to advances in hardware technology, high-end cameras can capture 3 or 5 different exposure images at the same time and fuse them into an HDR image automatically. However, there are still a few unsolved problems: the price of specialized cameras, moving objects, and camera angle stabilization [1, 2]. Many researchers have also developed software solutions to create an HDR image from a single LDR image. Inverse tone mapping (iTMO) [3] is a process that expands an LDR image to an HDR image for visualizing on a high dynamic range display. Several iTMO methods have been proposed based on mathematical models which infer color details in missing areas from LDR images [4, 5, 6]. However, those models have limitations that could not consider each image's individual characteristics and relationships between channels. Recent advances in deep learning neural networks for the computer vision field enable numerousapproaches to minimize these limitations. Deep Neural Networks, especially convolutional neural networks, help models to comprehend the complex nonlinearity between the input image and the target image, which is arduous for humans to detect [7; 8; 9; 10]. However, there are existing limitations. Expanding an 8-bit LDR image to a 32-bit HDR image is still challenging because of the lost details in over/undersaturated regions and too low or high exposure images [10; 11; 12; 13]. To solve these problems, we propose a method that lets a model find and concentrate on over/undersaturated regions. Additionally, the proposed method is able to convert the images which are captured by not only middle exposure but low or high exposure to HDR images. In this paper, we propose a convolutional neural network architecture, HSVNet, for reconstructing an HDR image from a single-exposure LDR input image. We utilize U-net with a mask and a customized loss function. For training, we make our own paired LDR-HDR image dataset by taking multiple-exposure LDR images and fusing them into an HDR image. We also gather a paired LDR-HDR image dataset from the image source [14]. In the network, the color space of the input image, which is usually RGB (Red, Green, and Blue), is converted to HSV (Hue, Saturation, and Value) color space. We use HSV color space in this paper because the HSV color space is designed to more closely represent the human perception of color. Since each channel in the HSV space represents a perceptual attribute of color, we believe that deep neural networks can learn the ground truth more concisely. Specifically, the \(V\) channel represents the brightness which is directly related to the high dynamic range of natural lights. Additionally, the hue of color in RGB space is determined by the ratio between three channels, which can make it difficult to reconstruct them. A better estimation is expected by separating these attributes. Reconstructing HDR in RGB color space may feature a weakness such as severe local inversion artifacts on reconstructed HDR images and insufficient contrast [2; 15; 16].