The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

纳米级磁自旋纹理的操纵和控制越来越引起人们的兴趣，因为它们是下一代计算范例中的潜在基础单元。要实现这一目标，需要使用原位实验定量了解外部刺激下的自旋纹理行为。洛伦兹透射电子显微镜 (LTEM) 能够实现纳米级自旋纹理的实空间成像，但原位数据的定量表征极具挑战性。在这里，我们提出了一种基于人工智能的相位检索方法，该方法基于将生成深度图像先验与 LTEM 图像形成前向模型相集成。与现有方法相比，我们的方法使用单个失焦图像进行相位检索，并实现了显着更高的准确性和对噪声的鲁棒性。此外，我们的方法能够从磁对比度中分离出样品的异质性，如模拟和实验数据的应用所示。这种方法允许对原位数据进行定量相位重建，并且还可以实现近实时定量磁成像。