DiTTo: Scalable Order-aware All-in-One Image Restoration Agent
Real-world images rarely suffer from a single degradation, and the order in which degradations are removed substantially affects the final restoration quality, motivating agent-based image restoration (IR), where a vision-language model schedules a pool of pre-built restoration-experts. However, existing training-based agents require $\mathcal{O}((N^{\mathbf{D}})^{2})$ restoration-expert calls per image to construct the Optimal Restoration-action Trajectory Dataset (ORTD), where $N^{\mathbf{D}}$ denotes the number of degradation types in the universe $\mathbf{D}$, and couple agent training to a fixed restoration-expert pool, preventing extension to newly introduced restoration-experts wit...
| Authors | Seungho Choi, Jihyong Oh |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 2 June 2026 |
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