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arXiv · 2606.19901

Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution

Mingyu Choi·Woo Kyoung Han·Sunghoon Im·Kyong Hwan Jin
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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 with computational complexity on par with existing methods. The source code and models are available at https://github.com/MingyuChoi-run/LSM

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Mingyu Choi, Woo Kyoung Han, Sunghoon Im, Kyong Hwan Jin. 2026-06-18. Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution. https://arxiv.org/abs/2606.19901

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