# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 2 Related Work  
Thanks to the advance of deep learning, there have been research works on low-light image enhancement based on deep neural networks [7; 8; 9; 10; 11; 12]. Some works build paired training data, i.e., low/normal-light image pairs, and use them to generate light-enhanced images. [PERSON] et al. introduced a See-in-the-Dark (SID) dataset of short-exposure low-light raw images with corresponding long-exposure reference raw images [9]. Their proposed  
Figure 1: An example picture of the Amalfi Coast (Photo by [PERSON] on Unsplash). model achieves high quality visual results. However, they require raw image data as well as paired low/normal-light training images. [PERSON] et al. built a multi-exposure image dataset including under/over-contrast and normal-contrast encoded image pairs, and proposed multi-exposure image fusion or high dynamic range algorithms to generate the contrast enhanced images [12]. [PERSON] et al. proposed EnlightenGAN, which is an unsupervised GAN trained without paired data [10]. It shows the feasibility of learning unpaired data for low-light enhancement. Although these techniques can be used to make low-light images look bright, it is difficult to use them for our purpose of transforming nighttime images to daytime images. It is because light-enhanced nighttime images generated by these techniques would be still nighttime images of which brightness is improved. There have been several studies on image-to-image translation techniques based on GANs [1; 2; 3; 4; 13; 14]. These techniques are largely divided into supervised and unsupervised learning depending on how the training data set is constructed. Image-to-image translation based on unsupervised learning that trains a model through unpaired data sets can be used more flexibly because of the advantage of being able to collect data sets easily. Widely known unsupervised image-to-image translation models include CycleGAN [1] and DiscoGAN [14]. These models share the same concept, using a cycle-consistent loss with unpaired data to translate between two different domains. The cycle-consistency loss is a constraint for maintaining consistency with the original domain when generating a fake image of another domain. Although they present a similar concept, they have a different focus. The main goal of CycleGAN is to translate the style of high-resolution images, so the network size is large. Although it shows a good performance of translating appearance such as color and texture, it is limited to translate the shape of an object involving geometric changes. On the other hand, DiscoGAN is designed with a relatively simple network structure for finding cross-domain relations. It shows good performance in translating the style of different shapes of objects (e.g., shoes to handbags), but its resolution is not large. Another well-known model is a UNIT employing an architecture that allows information to be shared between domains by applying a shared latent space [2]. In the shared latent space, corresponding images of different domains can be mapped to the same latent representation. UNIT shows a good performance in translation of human faces and animal breeds. In addition, there are many works of using unsupervised image translation models for cartoons, characters, and art works [15; 16; 17]. Our aim in this paper can be seen as a specific problem of image-to-image translation focusing on nighttime to daytime image translation. Recently, there have been a couple of works targeting this scope. [PERSON] et al. presented a high-resolution daytime translation model to generate daytime timelapse videos [18] exhibiting different times of the day and different lighting for a given input image. It presents a new architecture that combines an image-to-image translation model and a new upsampling scheme for high-resolution image generation. It minimizes the training time and generates output daytime images of high quality. [PERSON] et al. proposed a ToDayGAN to translate nighttime images to daytime images for a visual localization purpose [19].