用Transformer实现单次扫描快速高精度相位重构
PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval
- 基于分层Transformer处理衍射图块，局部推理后无缝拼接
- 比ePIE快3600倍，稀疏扫描下仍保持高质量重建
- 适合需要高速成像的显微成像与材料表征研究者
叠印成像是一种计算显微技术，通过一系列衍射图样恢复样品的高分辨率透射图像。传统相位重构算法需过采样衍射图样，计算成本高，且难以恢复样品透射函数的绝对相位。深度学习方法为突破迭代算法局限提供了新路径。本文提出PtychoFormer，一种基于分层Transformer的数据驱动单次扫描叠印相位重构模型。该模型处理衍射图样的子集，生成局部推断并无缝拼接，实现高质量重建。模型对稀疏扫描的衍射图样具有鲁棒性，相比扩展叠印迭代引擎（ePIE）提升达3600倍的成像速度。我们还提出扩展版PtychoFormer（ePF），融合PtychoFormer与ePIE的优势，有效抑制全局相位偏移，显著提升重建质量，在叠印成像中达到当前最优性能。
原文摘要 · Abstract (English)
Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. While conventional phase retrieval algorithms can iteratively recover the images, they require oversampled diffraction patterns, incur significant computational costs, and struggle to recover the absolute phase of the sample's transmission function. Deep learning algorithms for ptychography are a promising approach to resolving the limitations of iterative algorithms. We present PtychoFormer, a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval. PtychoFormer processes subsets of diffraction patterns, generating local inferences that are seamlessly stitched together to produce a high-quality reconstruction. Our model exhibits tolerance to sparsely scanned diffraction patterns and achieves up to 3600 times faster imaging speed than the extended ptychographic iterative engine (ePIE). We also propose the extended-PtychoFormer (ePF), a hybrid approach that combines the benefits of PtychoFormer with the ePIE. ePF minimizes global phase shifts and significantly enhances reconstruction quality, achieving state-of-the-art phase retrieval in ptychography.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。