Single image super-resolution (SISR) methods can enhance the resolution and quality of underwater images. Enhancing the resolution of underwater images leads to better performance of autonomous underwater vehicles (AUVs). In this work, we fine-tune the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) model to increase the resolution of underwater images. In our proposed approach, the pre-trained generator and discriminator networks of the Real-ESRGAN model are fine-tuned using underwater image datasets. We used USR-248 and UFO-120 datasets to fine-tune the Real-ESRGAN model. Our fine-tuned model produces images with better resolution and quality compared to the original model.

翻译：单一图像超分辨率(SISSR)方法可以提高水下图像的分辨率和质量。提高水下图像的分辨率可以提高水下图像的分辨率,从而改进自主水下飞行器的性能。在这项工作中,我们微调了实时增强的超分辨率生成反对流网络(Real-ESRGAN)模型,以提高水下图像的分辨率。在我们建议的方法中,Real-ESRGAN模型的预培训生成器和歧视器网络使用水下图像数据集进行微调。我们用USR-248和UFO-120数据集来微调Real-ESRGAN模型。我们的微调模型比原始模型产生更好的分辨率和质量图像。