Advancements in Single Image Super-Resolution Techniques

Le Dai

2024

Abstract

The technology of image super-resolution has been widely used in the industry for data recovery, graphic rendering and enhancing image quality. This paper offers a detailed overview of the progress made in Single Image Super Resolution (SISR) techniques, tracking the shift from traditional interpolation methods to cutting edge deep learning approaches like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). In the early stages of research, researchers proposed using interpolation calculations such as bilinear, cubic and b interpolations for image super-resolution, but these methods lacked to produce high-quality super-resolved images. With the advancement of machine learning, experts have introduced some super-resolution techniques like CNNs and GANs that have significantly improved the quality of super-resolved images. This paper delves into the impact of these advancements on various applications and explores future research avenues in SISR, emphasizing the potential for further enhancements in image quality and the development of new algorithms for diverse applications.

Paper Citation

in Harvard Style

Dai L. (2024). Advancements in Single Image Super-Resolution Techniques. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 160-167. DOI: 10.5220/0012832900004547

in Bibtex Style

@conference{icdse24,
author={Le Dai},
title={Advancements in Single Image Super-Resolution Techniques},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012832900004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Advancements in Single Image Super-Resolution Techniques
SN - 978-989-758-690-3
AU - Dai L.
PY - 2024
SP - 160
EP - 167
DO - 10.5220/0012832900004547
PB - SciTePress