Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration
Diffusion models have demonstrated remarkable effectiveness in image restoration tasks. However, when guiding image reconstruction, existing Diffusion Model-based Image Restoration (DMIR) methods typically rely on fixed data constraints and uniform step sizes, thereby overlooking the dynamic nature of the generative process. Such rigid designs render the models vulnerable to spatially non-uniform degradations, thus resulting in structural distortions and loss of fine details. Meanwhile, uniform step sizes introduce computational redundancy, whereas naïve step reduction strategies tend to accumulate approximation errors. To address these limitations, we propose a Local Epistemic Uncertaint...
| Authors | Jiaqi Zhang, Zheng Pang, Rongrong Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 6 October 2026 |
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