Super-Resolution Reconstruction of COVID-19 Images Based on Generative Adversarial Networks

Jiarui Zhou

2024

Abstract

In low brightness or light conditions, the images generated by existing medical equipment for testing patients often have problems such as low clarity, feature loss, and excessive noise. The accuracy and timeliness of medical image processing directly affect doctors' diagnosis and treatment. A series of image enhancement technologies can viably make strides in the quality of low-brightness pictures, making image features more obvious, and thereby helping doctors identify lesions faster and more accurately. Traditional image enhancement techniques work for general cases, while super-resolution with Convolutional Neural Networks (CNNs) needs large labeled datasets and often misses high-frequency details during large-scale upscaling. In contrast, Generative Adversarial Networks (GANs), such as unsupervised learning neural networks, can effectively solve such problems. The objective of this work is to reproduce low-resolution COVID-19 images to super-resolution through the Super-Resolution Generative Adversarial Network (SRGAN) model. The results of the experiment show that the model can perform super-resolution reconstruction of COVID-19 images well.

Paper Citation

in Harvard Style

Zhou J. (2024). Super-Resolution Reconstruction of COVID-19 Images Based on Generative Adversarial Networks. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 406-411. DOI: 10.5220/0013525000004619

in Bibtex Style

@conference{daml24,
author={Jiarui Zhou},
title={Super-Resolution Reconstruction of COVID-19 Images Based on Generative Adversarial Networks},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={406-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013525000004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Super-Resolution Reconstruction of COVID-19 Images Based on Generative Adversarial Networks
SN - 978-989-758-754-2
AU - Zhou J.
PY - 2024
SP - 406
EP - 411
DO - 10.5220/0013525000004619
PB - SciTePress