Lightweight Real-Time Image Super-Resolution Network for 4K Images

Ganzorig Gankhuyag, Kihwan Yoon, Jinman Park, Haeng Seon Son, Kyoungwon Min; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 1746-1755

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.

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[bibtex]
@InProceedings{Gankhuyag_2023_CVPR, author = {Gankhuyag, Ganzorig and Yoon, Kihwan and Park, Jinman and Son, Haeng Seon and Min, Kyoungwon}, title = {Lightweight Real-Time Image Super-Resolution Network for 4K Images}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {1746-1755} }