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

Ganzorig Gankhuyag, Kihwan Yoon, Jinman Park, Haeng Seon Son, Kyoungwon Min

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.