论文标题
使用基于生成对抗网络的模型的水下图像超分辨率
Underwater Image Super-Resolution using Generative Adversarial Network-based Model
论文作者
论文摘要
单图像超分辨率（SISR）模型能够增强水下图像的分辨率和视觉质量，并有助于更好地了解水下环境。这些模型在自动水下车辆（AUV）中的集成可以提高其在基于视觉的任务中的性能。实现的超分辨率生成对抗网络（Real-Esrgan）是一个有效的模型，在SISR模型中表现出了出色的性能。在本文中，我们微调了用于水下图像超分辨率的预训练的实体模型。要微调和评估模型的性能，我们使用USR-248数据集。与Real-Esrgan模型相比，微调模型可产生更现实的图像，具有更好的视觉质量。
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.
