用双域调制提升轻量图像超分辨率，速度更快、更省算力。
Dual-domain Modulation Network for Lightweight Image Super-Resolution
- 融合小波与傅里叶信息，实现频域特征互补建模。
- 相较SRFormer和MambaIR，FLOPs减半以下，推理速度提升5倍以上。
- 适合移动端或边缘设备部署，兼顾清晰度与效率。
轻量级图像超分辨率旨在低计算成本下从低分辨率图像重建高分辨率图像。我们发现现有基于频域的方法难以平衡整体结构与高频细节的重建，且处理频域特征效率低，不适用于轻量级场景。本文提出双域调制网络（DMNet），融合小波域与傅里叶域信息，增强频域建模能力。具体地，通过小波域调制变压器（WMT）进行小波域调制，并结合全局傅里叶监督，实现互补频谱学习，更适合轻量级超分辨率。实验表明，本方法在性能上可比肩SRFormer和MambaIR，FLOPs低于其50%和60%，推理速度分别提升15.4倍和5.4倍，验证了其在超分辨率质量与轻量化方面的有效性。代码已开源。
原文摘要 · Abstract (English)
Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images under limited computational costs. We find that existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts. Meanwhile, these methods are inefficient for handling frequency features and unsuitable for lightweight SR. In this paper, we show that introducing both wavelet and Fourier information allows our model to consider both high-frequency features and overall SR structure reconstruction while reducing costs. Specifically, we propose a Dual-domain Modulation Network that integrates both wavelet and Fourier information for enhanced frequency modeling. Unlike existing methods that rely on a single frequency representation, our design combines wavelet-domain modulation via a Wavelet-domain Modulation Transformer (WMT) with global Fourier supervision, enabling complementary spectral learning well-suited for lightweight SR. Experimental results show that our method achieves a comparable PSNR to SRFormer and MambaIR while with less than 50\% and 60\% of their FLOPs and achieving inference speeds 15.4x and 5.4x faster, respectively, demonstrating the effectiveness of our method on SR quality and lightweight. Code link: https://github.com/24wenjie-li/DMNet
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