Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration
All-in-one image restoration seeks a single model that can recover images degraded by diverse and spatially non-uniform corruptions. However, many unified Transformers rely on fixed patch partitioning: task/degradation condition is injected only into the backbone blocks after tokenization, leaving the embedding and reconstruction stages insensitive to local degradation variations. In contrast to previous approaches, we present Flexible Image Transformer (FIT) that explicitly models degradation awareness across the entire pipeline, from patch sampling to pixel reconstruction. Specifically, FIT employs a lightweight Degradation Encoder to predict a global degradation vector $\mathbf{g}$ and...
| Authors | Zihao He, Yunfeng Wu, Xinchao Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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