Dual-domain Modulation Network for Lightweight Image Super-Resolution
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912529171611648 |
|---|---|
| author | Li, Wenjie Guo, Heng Hou, Yuefeng Gao, Guangwei Ma, Zhanyu |
| author_facet | Li, Wenjie Guo, Heng Hou, Yuefeng Gao, Guangwei Ma, Zhanyu |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-domain Modulation Network for Lightweight Image Super-Resolution Li, Wenjie Guo, Heng Hou, Yuefeng Gao, Guangwei Ma, Zhanyu Image and Video Processing Computer Vision and Pattern Recognition 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 |
| title | Dual-domain Modulation Network for Lightweight Image Super-Resolution |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.10047 |