无位置信息也能重建图像，用扩散模型做先验实现突破
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
- 用扩散模型作图像先验，通过变分推断联合恢复位置与图像
- 在噪声存在下仍可成功重建，仅最极端情况失败
- 适合做衍射成像中未知位置的图像恢复研究者参考
本文研究了一种新型盲反问题——位置无关的叠印全息（position-blind ptychography），即在完全不知扫描位置的情况下进行相位恢复并同时重建图像。该问题源于单颗粒衍射X射线成像：随机取向的粒子被照射，收集一组衍射图样。若使用高度聚焦的X射线束，测量将对束斑相对于粒子的位置敏感，从而具有叠印特性，但这些位置同样未知。我们针对一个简化的二维模拟场景，采用基于得分的扩散模型作为数据驱动的图像先验，结合变分推断方法，评估了图像重建的可行性。结果表明，在合适的照明结构和强先验条件下，即使存在测量噪声，绝大多数情况下仍能实现可靠且成功的图像重建，仅在最严苛的测试场景中失败。
原文摘要 · Abstract (English)
In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions, which then must be recovered jointly with the image. The motivation for this problem comes from single-particle diffractive X-ray imaging, where particles in random orientations are illuminated and a set of diffraction patterns is collected. If one uses a highly focused X-ray beam, the measurements would also become sensitive to the beam positions relative to each particle and therefore ptychographic, but these positions are also unknown. We investigate the viability of image reconstruction in a simulated, simplified 2-D variant of this difficult problem, using variational inference with modern data-driven image priors in the form of score-based diffusion models. We find that, with the right illumination structure and a strong prior, one can achieve reliable and successful image reconstructions even under measurement noise, in all except the most difficult evaluated imaging scenario.
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