English(EN) PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

PatchDenoiser 为低剂量CT扫描提供参数高效去噪

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-30 04:00

研究人员开发了PatchDenoiser，一种新颖的、参数高效的低剂量CT图像去噪框架。该方法利用多尺度块学习和融合策略，在有效抑制噪声的同时保留精细的解剖细节，性能优于传统滤波和现有深度学习技术。PatchDenoiser的参数量显著减少，计算复杂度更低，使其成为医学图像去噪的实用且可扩展的解决方案。 AI

影响为医学图像去噪提供了一种更高效、更精细的解决方案，有望提高诊断准确性和患者安全性。

排序理由该集群包含一篇arXiv论文，详细介绍了一种新的医学图像去噪方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski · 2026-07-30 04:00

PatchDenoiser：用于低剂量CT成像的参数高效多尺度块学习与融合去噪器

arXiv:2602.21987v3 Announce Type: replace Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient moti…

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PatchDenoiser：用于低剂量CT成像的参数高效多尺度块学习与融合去噪器

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