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.
This work was supported by Institute of Information \& communications Technology Planning \& Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2021-0-00800, Development of Driving Environment Data Transformation and Data Verification Technology for the Mutual Utilization of Self-driving Learning Data for Different Vehicles)
@inproceedings{gankhuyag2023lightweight,
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={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={1746--1755},
year={2023}
}