Two Heads Better than One: Dual Degradation Representation for Blind 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_ | 1866908680582070272 |
|---|---|
| author | Yuan, Hsuan Weng, Shao-Yu Lo, I-Hsuan Chiu, Wei-Chen Xu, Yu-Syuan Hsueh, Hao-Chien Chuang, Jen-Hui Huang, Ching-Chun |
| author_facet | Yuan, Hsuan Weng, Shao-Yu Lo, I-Hsuan Chiu, Wei-Chen Xu, Yu-Syuan Hsueh, Hao-Chien Chuang, Jen-Hui Huang, Ching-Chun |
| contents | Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from these assumptions, these methods may experience significant declines in performance. In this paper, we propose a Dual Branch Degradation Extractor Network to address the blind SR problem. While some blind SR methods assume noise-free degradation and others do not explicitly consider the presence of noise in the degradation model, our approach predicts two unsupervised degradation embeddings that represent blurry and noisy information. The SR network can then be adapted to blur embedding and noise embedding in distinct ways. Furthermore, we treat the degradation extractor as a regularizer to capitalize on differences between SR and HR images. Extensive experiments on several benchmarks demonstrate our method achieves SOTA performance in the blind SR problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two Heads Better than One: Dual Degradation Representation for Blind Super-Resolution Yuan, Hsuan Weng, Shao-Yu Lo, I-Hsuan Chiu, Wei-Chen Xu, Yu-Syuan Hsueh, Hao-Chien Chuang, Jen-Hui Huang, Ching-Chun Computer Vision and Pattern Recognition Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from these assumptions, these methods may experience significant declines in performance. In this paper, we propose a Dual Branch Degradation Extractor Network to address the blind SR problem. While some blind SR methods assume noise-free degradation and others do not explicitly consider the presence of noise in the degradation model, our approach predicts two unsupervised degradation embeddings that represent blurry and noisy information. The SR network can then be adapted to blur embedding and noise embedding in distinct ways. Furthermore, we treat the degradation extractor as a regularizer to capitalize on differences between SR and HR images. Extensive experiments on several benchmarks demonstrate our method achieves SOTA performance in the blind SR problem. |
| title | Two Heads Better than One: Dual Degradation Representation for Blind Super-Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16963 |