Title : ( Underwater Image Super-Resolution using Generative Adversarial Network-based Model )
Authors: alireza aghelan , Modjtaba Rouhani ,
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Abstract
Single image super-resolution (SISR) models are able to enhance the 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-ESRGAN is a powerful model that has shown remarkable performance among SISR models. In this paper, we optimize the Real-ESRGAN model for underwater image super-resolution. To optimize and evaluate the performance of the model, we use the USR-248 dataset. The proposed model generates images that demonstrate a higher level of visual quality than the outputs of the Real-ESRGAN model.
Keywords
@inproceedings{paperid:1098691,
author = {Aghelan, Alireza and Rouhani, Modjtaba},
title = {Underwater Image Super-Resolution using Generative Adversarial Network-based Model},
booktitle = {2023 13th International Conference on Computer and Knowledge Engineering (ICCKE)},
year = {2023},
location = {مشهد, IRAN},
}
%0 Conference Proceedings
%T Underwater Image Super-Resolution using Generative Adversarial Network-based Model
%A Aghelan, Alireza
%A Rouhani, Modjtaba
%J 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE)
%D 2023
