LSM-pretrained

Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution

This repository contains the pre-trained weights for LSM, presented in the paper Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution.

• GitHub Repository: https://github.com/MingyuChoi-run/LSM
• Paper: https://arxiv.org/abs/2606.19901

Abstract

Linear recurrent unit (LRU), designed with a principled formulation for stable linear recurrence, has demonstrated promising accuracy and robustness on long-range dependency tasks. However, its static parameterization and single-scan method limits its applicability to 2D vision tasks. In this study, we propose a LRU-based restoration network with a semantic modulating unit (SMU) to achieve a harmonious balance between performance and efficiency in single-image super-resolution. The SMU plays three key roles: LRU modulation, spatial categorization, and feature enhancement through learned prototype. Extensive experiments demonstrate that our method quantitatively and qualitatively surpasses recent state-of-the-art methods. Notably, our approach achieves superior performance while maintaining computational complexity on par with existing efficient methods.

Citation

@InProceedings{Choi_2026_CVPR,
    author    = {Choi, Mingyu and Han, Woo Kyoung and Im, Sunghoon and Jin, Kyong Hwan},
    title     = {Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings},
    month     = {June},
    year      = {2026},
    pages     = {4950-4960}
}

Acknowledgements

This code is built on BasicSR.