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

• URL: http://arxiv.org/abs/2211.03550v4
• Date: Sat, 23 Sep 2023 16:14:20 GMT
• Title: Underwater Image Super-Resolution using Generative Adversarial Network-based Model
• Authors: Alireza Aghelan, Modjtaba Rouhani
• Abstract summary: Single image super-resolution (SISR) models are able to enhance the resolution and visual quality of underwater images. In this paper, we fine-tune the pre-trained Real-ESRGAN model for underwater image super-resolution.
• Score: 3.127436744845925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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