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English(EN) ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising

新的ENCORE框架增强了低剂量CT图像去噪能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为ENCORE的新框架，用于去噪低剂量计算机断层扫描（CT）图像。该框架明确考虑了CT扫描独特的噪声特性，超越了通用的图像去噪模型。ENCORE基于更真实的噪声分布重新构建了噪声合成，并提取了局部噪声功率和相关性上下文。它还引入了一个FlyingConv模块，可以自适应地调整不同图像区域的卷积权重，从而提高去噪质量和计算效率。此外，ENCORE通过在推理时操纵噪声上下文图，实现了零样本条件去噪，从而能够动态控制输出图像纹理。 AI

排序理由 该集群包含一篇详细介绍新图像去噪框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的ENCORE框架增强了低剂量CT图像去噪能力

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该集群包含一篇详细介绍新图像去噪框架的研究论文。[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) · Minwoo Yu, N. Robert Bennett, Jongduk Baek, Adam S. Wang ·

    ENCORE：低剂量CT去噪的高效噪声感知表征

    arXiv:2608.10343v1 Announce Type: new Abstract: While deep learning-based denoising has become widely adopted in low-dose CT, conventional models use generic architectures designed for natural images, failing to account for non-stationary and spatially correlated CT noise charact…

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