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arXiv Computer Vision By Jiaqi Zhang, Zheng Pang, Rongrong Gao, Qiyuan Zhang, Yang Yang

Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration

Read the original on arXiv Computer Vision →

The paper introduces LEADer, a framework that uses local epistemic uncertainty to guide active sampling in diffusion-based image restoration. By adjusting prior strength per pixel and pruning sampling trajectories based on uncertainty traces, LEADer balances detail preservation with artifact suppression and accelerates convergence. The method is plug‑and‑play, theoretically guarantees data consistency and stable convergence, and improves performance across multiple state‑of‑the‑art diffusion models with minimal memory overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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