Underwater Image Super-Resolution using Generative Adversarial Network-based Model

Single image super-resolution (SISR) models are able to enhance the visual quality of underwater images and contribute to a better understanding of underwater environments. The integration of these models in Autonomous Underwater Vehicles (AUVs) can improve their performance in vision-based tasks. Real-ESRGAN is a powerful model that has shown remarkable performance among SISR models. In this paper, we optimize the Real-ESRGAN model for underwater image super-resolution. To optimize and evaluate the performance of the model, we use the USR-248 dataset. The proposed model generates images that demonstrate a higher level of visual quality than the outputs of the Real-ESRGAN model.