SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908869990547456 |
|---|---|
| author | Shurui, Peng Lin, Xin Luo, Shi Ou, Jincen Zhang, Dizhe Qi, Lu Nguyen, Truong Ren, Chao |
| author_facet | Shurui, Peng Lin, Xin Luo, Shi Ou, Jincen Zhang, Dizhe Qi, Lu Nguyen, Truong Ren, Chao |
| contents | Image restoration under diverse degradations remains challenging for unified all-in-one frameworks due to feature interference and insufficient expert specialization. We propose SLER-IR, a spherical layer-wise expert routing framework that dynamically activates specialized experts across network layers. To ensure reliable routing, we introduce a Spherical Uniform Degradation Embedding with contrastive learning, which maps degradation representations onto a hypersphere to eliminate geometry bias in linear embedding spaces. In addition, a Global-Local Granularity Fusion (GLGF) module integrates global semantics and local degradation cues to address spatially non-uniform degradations and the train-test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achieves consistent improvements over state-of-the-art methods in both PSNR and SSIM. Code and models will be publicly released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05940 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration Shurui, Peng Lin, Xin Luo, Shi Ou, Jincen Zhang, Dizhe Qi, Lu Nguyen, Truong Ren, Chao Computer Vision and Pattern Recognition Image restoration under diverse degradations remains challenging for unified all-in-one frameworks due to feature interference and insufficient expert specialization. We propose SLER-IR, a spherical layer-wise expert routing framework that dynamically activates specialized experts across network layers. To ensure reliable routing, we introduce a Spherical Uniform Degradation Embedding with contrastive learning, which maps degradation representations onto a hypersphere to eliminate geometry bias in linear embedding spaces. In addition, a Global-Local Granularity Fusion (GLGF) module integrates global semantics and local degradation cues to address spatially non-uniform degradations and the train-test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achieves consistent improvements over state-of-the-art methods in both PSNR and SSIM. Code and models will be publicly released. |
| title | SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.05940 |