用无监督深度先验提升稀疏视角电子断层成像质量
Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography
- 不依赖标注数据，直接利用网络结构自身先验重建图像
- 60°倾斜范围、10°步进角下仍达到与有监督方法相当效果
- 适合实验条件受限的纳米材料三维重构，尤其缺数据场景
电子断层成像（ET）在纳米材料三维表征中至关重要。但在有限角度和稀疏视角条件下，传统算法重建质量下降，影响3D数据的可用性。本文提出无监督深度图像先验（DIP）方法，无需训练数据即可处理严重退化的断层数据。模拟结果显示，在仅60°倾斜范围、10°步进角下，其性能可媲美需训练数据的监督方法。进一步应用于真实实验数据，证实该方法在稀疏视角与有限角度条件下仍能实现可靠的3D定量分析，展现出对多种材料及采集模式的广泛适用性。
原文摘要 · Abstract (English)
Electron tomography (ET) plays an important role in the three-dimensional (3D) characterization of nanomaterials. However, under limited-angle and sparse-view conditions, conventional algorithms produce degraded reconstructions, which compromise the quality and interpretability of resulting 3D data. In this paper, we present deep image prior (DIP), an unsupervised deep learning (DL) approach, for highly degraded tomography acquisitions and demonstrate, using simulated data, that its performance is comparable to that of supervised approaches requiring training datasets, even for tilt ranges as limited as 60° and tilt increments of 10°. We then apply it to experimental data and show that it enables reliable 3D quantification under both sparse-view and limited-angle conditions, highlighting its potential for a wide range of materials and acquisition modalities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。