Large Kernel Modulation Network for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2508.11893v1
• Date: Sat, 16 Aug 2025 03:43:14 GMT
• Title: Large Kernel Modulation Network for Efficient Image Super-Resolution
• Authors: Quanwei Hu, Yinggan Tang, Xuguang Zhang,
• Abstract summary: Large Kernel Modulation Network (LKMN) is a pure CNN-based model.<n>LKMN has two core components: Enhanced Partial Large Kernel Block (EPLKB) and Cross-Gate Feed-Forward Network (CGFN)<n>LKMN-L achieves 0.23 dB PSNR improvement over DAT-light on the Manga109 dataset at $times$4 upscale, with nearly $times$4.8 times faster.
• Score: 5.875680381119361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image super-resolution (SR) in resource-constrained scenarios demands lightweight models balancing performance and latency. Convolutional neural networks (CNNs) offer low latency but lack non-local feature capture, while Transformers excel at non-local modeling yet suffer slow inference. To address this trade-off, we propose the Large Kernel Modulation Network (LKMN), a pure CNN-based model. LKMN has two core components: Enhanced Partial Large Kernel Block (EPLKB) and Cross-Gate Feed-Forward Network (CGFN). The EPLKB utilizes channel shuffle to boost inter-channel interaction, incorporates channel attention to focus on key information, and applies large kernel strip convolutions on partial channels for non-local feature extraction with reduced complexity. The CGFN dynamically adjusts discrepancies between input, local, and non-local features via a learnable scaling factor, then employs a cross-gate strategy to modulate and fuse these features, enhancing their complementarity. Extensive experiments demonstrate that our method outperforms existing state-of-the-art (SOTA) lightweight SR models while balancing quality and efficiency. Specifically, LKMN-L achieves 0.23 dB PSNR improvement over DAT-light on the Manga109 dataset at $\times$4 upscale, with nearly $\times$4.8 times faster. Codes are in the supplementary materials. The code is available at https://github.com/Supereeeee/LKMN.

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