A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging

• URL: http://arxiv.org/abs/2412.06806v2
• Date: Fri, 02 May 2025 05:24:06 GMT
• Title: A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging
• Authors: Han Yue, Jun Cheng, Yu-Xuan Ren, Chien-Chun Chen, Grant A. van Riessen, Philip Heng Wai Leong, Steve Feng Shu,
• Abstract summary: Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns.<n>We present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging.
• Score: 8.436077464774755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns. Conventional neural architectures, both convolutional neural networks and Transformer-based methods, are optimized for natural images with Euclidean spatial neighborhood-based inductive biases that exhibit geometric mismatch with the concentric coherent patterns characteristic of diffraction data in reciprocal space. In this paper, we present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging, that aligns neural inductive biases with diffraction physics through a dual-branch architecture separating local feature extraction from non-local coherence modeling. It consists of a PoCA mechanism that replaces Euclidean spatial priors with physically consistent radial-angular correlations. PPN outperforms existing end-to-end models, with spectral and spatial analysis confirming its greater preservation of high-frequency details. Notably, PPN maintains robust performance compared to iterative methods even at low overlap ratios, making it well suited for high-throughput imaging in real-world acquisition scenarios for samples with consistent structural characteristics.

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