A comparative study of deep learning models for image super-resolution

Jia You Lim; Yeong Shiong Chiew; Raphaël C.-W. Phan; Xin Wang

doi:10.1117/12.3032724

27 June 2024 A comparative study of deep learning models for image super-resolution

Jia You Lim, Yeong Shiong Chiew, Raphaël C.-W. Phan, Xin Wang

Proceedings Volume 13211, Asia Conference on Electronic Technology (ACET 2024); 1321105 (2024) https://doi.org/10.1117/12.3032724
Event: Asia Conference on Electronic Technology (ACET 2024), 2024, Singapore, Singapore

Abstract

Many deep learning-based image super-resolution models exist to effectively up-sample images, with the most notable and reliable architectures being Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs). To date, model benchmarking has been made only with the same architecture type or only with certain datasets that could potentially be beneficial to the proposed models. In this paper, we present the first-known comparison of state-of-the-art super-resolution models, namely, SwinIR, EDSR, Swin2SR and Real- ESRGAN, to serve as a reference baseline for future applications where the modelling complexity, frame rates and overall super-resolution accuracy is of concern. The experiments were conducted by reproducing the models entirely by following the training procedures highlighted in their original paper. Then, we performed the evaluations on the conventional image super-resolution test sets, namely, Set5, Set14, BSD100, Urban100, T91 and Manga109. Our experimental results show that each model has their respective tradeoff between the number of measures taken to suppress the super-resolution artifacts and achieve a higher super-resolution accuracy and the overall model processing times, such as the model convergence speed and their respective frame rates.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia You Lim, Yeong Shiong Chiew, Raphaël C.-W. Phan, and Xin Wang "A comparative study of deep learning models for image super-resolution", Proc. SPIE 13211, Asia Conference on Electronic Technology (ACET 2024), 1321105 (27 June 2024); https://doi.org/10.1117/12.3032724

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Super resolution

Education and training

Data modeling

RGB color model

Performance modeling

Transformers

Image enhancement

Show All Keywords

Keywords/Phrases

Search In:

Publication Years