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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
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