Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning

2026-09-12

AI Summary

该研究提出一种基于深度学习的直接局部到全局学习框架，用于从衍射测量中重建全视野相位图，无需迭代优化，解决了传统方法计算成本高的问题。

Abstract

Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in substantial computational cost for large-scale 4D-STEM data. We present a direct local-to-global learning framework that reconstructs full-field phase maps from diffraction measurements without iterative refinement during inference. The proposed network predicts local wrapped-phase patches from individual diffraction patterns using a sine-cosine representation, and the predictions are assembled into a full-field reconstruction using calibrated scan positions and Gaussian-weighted stitching. To evaluate generalization beyond the training domain, the model is trained on one material and directly applied to another in a zero-shot setting without target-domain fine-tuning. Experiments on AuPd and MoS$_2$ demonstrate consistent cross-material transfer in both directions, with the proposed method achieving the best full-field MSE, PSNR, and MS-SSIM among the evaluated learning-based methods. Compared with the iterative ePIE approach, the proposed direct local-to-global pipeline reduces end-to-end reconstruction time by approximately 10x, demonstrating its potential for efficient and transferable ptychographic reconstruction.

Problem

• Ptychographic Phase Reconstruction
• Deep Learning
• Zero-Shot
• Cross-Material Generalization
• Computational Cost

Innovation

• direct local-to-global learning framework
• sine-cosine representation
• zero-shot cross-material transfer
• Gaussian-weighted stitching