Lightweight Real-Time Image Super-Resolution Network for 4k Images
Abstract
Single-image super-resolution technology has become a topic of extensive research in various applications, aiming to enhance the quality and resolution of degraded images obtained from low-resolution sensors. However, most existing studies on single-image super-resolution have primarily focused on developing deep learning networks operating on high-performance graphics processing units. Therefore, this study proposes a lightweight real-time image super-resolution network for 4K images. Furthermore, we applied a reparameterization method to improve the network performance without incurring additional computational costs. The experimental results demonstrate that the proposed network achieves a PSNR of 30.15 dB and an inference time of 4.75 ms on an RTX 3090Ti device, as evaluated on the NTIRE 2023 Real-Time Super-Resolution validation scale X3 dataset. The code is available at https://github.com/Ganzooo/LRSRN.git.
Cite
Text
Gankhuyag et al. "Lightweight Real-Time Image Super-Resolution Network for 4k Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00175Markdown
[Gankhuyag et al. "Lightweight Real-Time Image Super-Resolution Network for 4k Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/gankhuyag2023cvprw-lightweight/) doi:10.1109/CVPRW59228.2023.00175BibTeX
@inproceedings{gankhuyag2023cvprw-lightweight,
title = {{Lightweight Real-Time Image Super-Resolution Network for 4k Images}},
author = {Gankhuyag, Ganzorig and Yoon, Kihwan and Park, Jinman and Son, Haeng Seon and Min, Kyoungwon},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {1746-1755},
doi = {10.1109/CVPRW59228.2023.00175},
url = {https://mlanthology.org/cvprw/2023/gankhuyag2023cvprw-lightweight/}
}