标题： 通过自动微分实现ptychography中衍射图样位移的亚像素校正 显示英文标题

标题： Subpixel correction of diffraction pattern shifts in ptychography via automatic differentiation

摘要： 全息衍射成像技术，一种相干衍射成像技术，由于其能够高分辨率、无透镜重建复数值图像，已成为材料表征、生物成像和纳米结构分析中不可或缺的工具。 在典型的流程中，原始衍射图样通常会被裁剪以隔离有效的中心区域以便于重建。 然而，如果裁剪位置偏离了衍射图样的零阶，重建可能会导致收敛速度变慢、相位包裹和图像保真度降低。 在涉及反射几何结构或宽频照明的实验配置中，这些问题进一步加剧，其中错误的裁剪会引入系统性的预处理误差，从而影响整个全息重建过程。 为了解决这一挑战，我们提出了一种基于自动微分（AD）的方法，其中裁剪偏移被视为重建框架中的可优化参数。 通过将偏移校正集成到反向传播循环中，我们的方法同时优化物体、探针和偏移位置，而无需手动调整。 仿真结果表明，即使初始偏移高达5像素，所提出的方法也能实现亚像素级校正，平均偏差低于0.5像素。 在极端紫外（EUV）波段的实验进一步验证了该方法的鲁棒性和有效性。 这种基于AD的策略提高了全息重建的自动化程度和鲁棒性，并且适用于多种实验条件。 显示英文摘要

摘要： Ptychography, a coherent diffraction imaging technique, has become an indispensable tool in materials characterization, biological imaging, and nanostructure analysis due to its capability for high-resolution, lensless reconstruction of complex-valued images. In typical workflows, raw diffraction patterns are commonly cropped to isolate the valid central region before reconstruction. However, if the crop is misaligned from the diffraction pattern's zero-order, reconstruction may suffer from slower convergence, phase wrapping, and reduced image fidelity. These issues are further exacerbated in experimental configurations involving reflective geometries or broadband illumination, where incorrect cropping introduces systematic preprocessing errors that compromise the entire ptychographic inversion. To address this challenge, we present an approach based on automatic differentiation (AD), where the cropping shift is treated as an optimizable parameter within the reconstruction framework. By integrating shift correction into the backpropagation loop, our method simultaneously refines the object, probe, and shift positions without requiring manual tuning. Simulation results demonstrate that, even with initial offsets ranging up to 5 pixels, the proposed method achieves subpixel correction, with an average deviation below 0.5 pixels. Experiments in the extreme ultraviolet (EUV) regime further validate the method's robustness and effectiveness. This AD-based strategy enhances the automation and robustness of ptychographic reconstructions, and is adaptable to diverse experimental conditions.