SRGAN in underwater vision

In recent years, the rapid industrialization of the world has led to an increasing importance of energy minerals. However, due to the scarcity of mineral resources, opportunities to rely on alternative energy are escalating. As a result, exploration of ocean resources, which exist abundantly in the sea, is being pursued. However, the manual exploration of ocean resources by diving and visually searching is dangerous and impractical. Therefore, it is pertinent to safely advance underwater exploration by having robots perform the work instead. In underwater environments, robots are commonly used as a mainstream exploration tool due to the various hazardous environmental conditions. However, there are several problems with controlling robots in underwater environments, and one of them is poor visibility underwater. Therefore, to improve visibility underwater, efforts are being made to achieve high resolution using super-resolution technology on underwater images. In this paper we first introduce the general model and architecture in GAN. Then we combine the GAN modal and characteristics of the underwater environment, elaborating how ESRGAN can be suitable for such circumstance. For data from ECCV2018 PIRM-SR, ESRGAN outperforms other traditional model like EnhanceNet [1], EDSR [2], RCAN [3], at least 24 % [4]. Such model can be equipped with robotics that highly depends on the resolution of the image, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs).