Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these issues, we propose our Omni-Scale RWKV Super-Resolution (OmniRWKVSR) model which presents a novel approach that combines the Receptance Weighted Key Value (RWKV) architecture with feature extraction techniques such as Visual RWKV Spatial Mixing (VRSM) and Visual RWKV Channel Mixing (VRCM), aiming to overcome the limitations of existing methods and achieve superior SISR performance. This work has proved able to provide effective solutions for high-quality image reconstruction. Under the 4x Super-Resolution tasks, compared to the MambaIR model, we achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM.
@article{arxiv.2502.00404,
title = {Exploring Linear Attention Alternative for Single Image Super-Resolution},
author = {Rongchang Lu and Changyu Li and Donghang Li and Guojing Zhang and Jianqiang Huang and Xilai Li},
journal= {arXiv preprint arXiv:2502.00404},
year = {2025}
}
Comments
This paper has been published to IEEE International Joint Conference on Neural Networks 2025 as the final camera ready version. Contact at nomodeset@qq.com