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

• URL: http://arxiv.org/abs/2609.13969v1
• Date: Sat, 12 Sep 2026 14:36:06 GMT
• Title: Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning
• Abstract summary: We present a direct local-to-global learning framework that reconstructs full-field phase maps from diffraction measurements without iterative refinement during inference.<n>The proposed network predicts local wrapped-phase patches from individual diffraction patterns using a sine-cosine representation.
• Score: 8.392466989483722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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