在角度分布未知时，联合估计二维断层图像与视角分布。
Two-Dimensional Unknown View Tomography from Unknown Angle Distributions
- 通过交叉验证误差优化，交替估计视角分布和图像结构。
- 在噪声投影下实现近似完美排序，优于直观基线方法。
- 适用于冷冻电镜等视角未知场景，适合成像重建研究者。
本研究提出一种在视角分布未知条件下的二维断层成像技术。该问题常见于冷冻电镜及CT系统几何标定中。尽管2D未知视角断层成像（UVT）已有一定研究基础，但多数现有算法依赖已知的视角分布，而实际中往往不可得。本文将问题建模为基于交叉验证误差的优化任务，采用交替策略联合估计视角分布与二维结构。探索了两种概率模型：半参数von Mises密度混合模型与概率质量函数模型。在含噪声投影条件下，结合基于PCA的去噪技术与由估计分布顺序统计量驱动的图拉普拉斯断层成像（GLT），确保近似完美排序，并与直观基线进行对比，验证算法有效性。
原文摘要 · Abstract (English)
This study presents a technique for 2D tomography under unknown viewing angles when the distribution of the viewing angles is also unknown. Unknown view tomography (UVT) is a problem encountered in cryo-electron microscopy and in the geometric calibration of CT systems. There exists a moderate-sized literature on the 2D UVT problem, but most existing 2D UVT algorithms assume knowledge of the angle distribution which is not available usually. Our proposed methodology formulates the problem as an optimization task based on cross-validation error, to estimate the angle distribution jointly with the underlying 2D structure in an alternating fashion. We explore the algorithm's capabilities for the case of two probability distribution models: a semi-parametric mixture of von Mises densities and a probability mass function model. We evaluate our algorithm's performance under noisy projections using a PCA-based denoising technique and Graph Laplacian Tomography (GLT) driven by order statistics of the estimated distribution, to ensure near-perfect ordering, and compare our algorithm to intuitive baselines.
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