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English(EN) FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring

FideDiff模型提供高效、高保真的图像去模糊功能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FideDiff，一种新颖的单步扩散模型，专为高保真图像运动去模糊而设计。该模型将去模糊重新构建为类似扩散的过程，其中一致性模型将渐进模糊的图像对齐到一张清晰的图像。通过集成Kernel ControlNet进行模糊核估计和自适应时间步预测，FideDiff在全参考指标上取得了卓越的性能，优于之前的基于扩散的方法，并与其他最先进的模型相媲美。 AI

影响 引入了一种更高效的图像恢复扩散模型，可能改善实际应用。

排序理由 这是一篇详细介绍新型图像去模糊模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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FideDiff模型提供高效、高保真的图像去模糊功能

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这是一篇详细介绍新型图像去模糊模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaoyang Liu, Zhengyan Zhou, Zihang Xu, Jiezhang Cao, Zheng Chen, Yulun Zhang ·

    FideDiff：高效扩散模型用于高保真图像运动去模糊

    arXiv:2510.01641v3 Announce Type: replace Abstract: Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-world modeling, have shown great promise for high-…

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