Frequency-Integrated Transformer for Arbitrary-Scale Super-Resolution
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918001072144384 |
|---|---|
| author | Wang, Xufei Ge, Fei Zhu, Jinchen Zhang, Mingjian Wu, Qi Weng, Jifeng Ren Shizhuang |
| author_facet | Wang, Xufei Ge, Fei Zhu, Jinchen Zhang, Mingjian Wu, Qi Weng, Jifeng Ren Shizhuang |
| contents | Methods based on implicit neural representation have demonstrated remarkable capabilities in arbitrary-scale super-resolution (ASSR) tasks, but they neglect the potential value of the frequency domain, leading to sub-optimal performance. We proposes a novel network called Frequency-Integrated Transformer (FIT) to incorporate and utilize frequency information to enhance ASSR performance. FIT employs Frequency Incorporation Module (FIM) to introduce frequency information in a lossless manner and Frequency Utilization Self-Attention module (FUSAM) to efficiently leverage frequency information by exploiting spatial-frequency interrelationship and global nature of frequency. FIM enriches detail characterization by incorporating frequency information through a combination of Fast Fourier Transform (FFT) with real-imaginary mapping. In FUSAM, Interaction Implicit Self-Attention (IISA) achieves cross-domain information synergy by interacting spatial and frequency information in subspace, while Frequency Correlation Self-attention (FCSA) captures the global context by computing correlation in frequency. Experimental results demonstrate FIT yields superior performance compared to existing methods across multiple benchmark datasets. Visual feature map proves the superiority of FIM in enriching detail characterization. Frequency error map validates IISA productively improve the frequency fidelity. Local attribution map validates FCSA effectively captures global context. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Frequency-Integrated Transformer for Arbitrary-Scale Super-Resolution Wang, Xufei Ge, Fei Zhu, Jinchen Zhang, Mingjian Wu, Qi Weng, Jifeng Ren Shizhuang Machine Learning Methods based on implicit neural representation have demonstrated remarkable capabilities in arbitrary-scale super-resolution (ASSR) tasks, but they neglect the potential value of the frequency domain, leading to sub-optimal performance. We proposes a novel network called Frequency-Integrated Transformer (FIT) to incorporate and utilize frequency information to enhance ASSR performance. FIT employs Frequency Incorporation Module (FIM) to introduce frequency information in a lossless manner and Frequency Utilization Self-Attention module (FUSAM) to efficiently leverage frequency information by exploiting spatial-frequency interrelationship and global nature of frequency. FIM enriches detail characterization by incorporating frequency information through a combination of Fast Fourier Transform (FFT) with real-imaginary mapping. In FUSAM, Interaction Implicit Self-Attention (IISA) achieves cross-domain information synergy by interacting spatial and frequency information in subspace, while Frequency Correlation Self-attention (FCSA) captures the global context by computing correlation in frequency. Experimental results demonstrate FIT yields superior performance compared to existing methods across multiple benchmark datasets. Visual feature map proves the superiority of FIM in enriching detail characterization. Frequency error map validates IISA productively improve the frequency fidelity. Local attribution map validates FCSA effectively captures global context. |
| title | Frequency-Integrated Transformer for Arbitrary-Scale Super-Resolution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.18818 |