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扩散模型在约束优化和CT去噪方面取得进展

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇研究论文探讨了扩散模型在图像去噪任务中的进展。第一篇论文《Denoising as Projection》提出一种方法，将预训练的去噪器用作对学习到的数据几何的近似投影，以实现约束优化。第二篇论文《FoundDiff》引入了一个基础扩散模型，用于可泛化的低剂量计算机断层扫描（CT）去噪，采用了两阶段策略来实现剂量和解剖感知以及自适应去噪。 AI

影响 这些论文推动了扩散模型在约束优化和专业图像去噪任务方面的能力，有可能改进科学研究和医学成像中的应用。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了扩散模型的新方法。

在 arXiv cs.AI 阅读 →

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扩散模型在约束优化和CT去噪方面取得进展

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两篇在arXiv上发表的学术论文，详细介绍了扩散模型的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Runyu Zhang, Jiawei Zhang, Gioele Zardini, Saurabh Amin, Asuman Ozdaglar ·

    去噪即投影：梯度引导扩散的约束优化

    arXiv:2608.29507v1 Announce Type: cross Abstract: Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-specific objectives. A common approach is to guide the reverse diffusion process usin…

  2. arXiv cs.CV TIER_1 English(EN) · Zhihao Chen, Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, Jun Zhao, Hongming Shan ·

    FoundDiff：用于可泛化低剂量CT去噪的基础扩散模型

    arXiv:2508.17299v2 Announce Type: replace Abstract: Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existin…

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