English(EN) One Step Is Optimal: Unconditional Rectified Flows as Noise2Noise Denoisers for Low-Dose CT

校正流在无需干净目标的情况下显示出CT去噪潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员探索了使用无条件校正流作为低剂量CT扫描去噪的方法，而无需干净的训练目标。他们的研究结果表明，校正流的一步积分是实现最小均方误差的最优选择，而进一步的积分步骤实际上会增加误差。实验表明，这种一步法在Noise2Noise对上进行直接回归时表现相当，优于额外的欧拉步。虽然监督卷积神经网络（CNN）取得了显著的进步，但无标签性能高度依赖于训练对如何处理相关的CT噪声，并且保真度增益并不总是能转化为可检测病灶的改善。 AI

影响这项研究可能通过实现低剂量CT扫描的更有效的去噪，而无需大量干净数据，从而改进医学成像技术。

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arXiv cs.CV TIER_1 English(EN) · Timothy Sereda, Debesh Jha · 2026-11-02 05:00

One Step Is Optimal: Unconditional Rectified Flows as Noise2Noise Denoisers for Low-Dose CT

arXiv:2609.31670v2 Announce Type: replace Abstract: We study rectified flows for denoising without clean training targets. For an unconditional rectified flow trained on Noise2Noise pairs, the optimal velocity field yields the minimum mean squared error (MMSE) denoiser in one ste…

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One Step Is Optimal: Unconditional Rectified Flows as Noise2Noise Denoisers for Low-Dose CT

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