  Single image super-resolution (SISR) models are able to enhance the
resolution and 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-Enhanced Super-Resolution Generative Adversarial
Network (Real-ESRGAN) is an efficient model that has shown remarkable
performance among SISR models. In this paper, we fine-tune the pre-trained
Real-ESRGAN model for underwater image super-resolution. To fine-tune and
evaluate the performance of the model, we use the USR-248 dataset. The
fine-tuned model produces more realistic images with better visual quality
compared to the Real-ESRGAN model.

Computer Vision and Pattern Recognition;Image and Video Processing