Underwater Image Super-Resolution using Generative Adversarial Network-based Model
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 7 Nov 2022.
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References · 8
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
- 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
- 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
- 2020Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perceptioncited by 53
- 2019Underwater Image Super-Resolution using Deep Residual Multiplierscited by 7
- 2021A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminatorscited by 6
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