Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe. The image recovery from ptychographic data is generally achieved via an iterative algorithm that solves a nonlinear phase-field problem derived from measured diffraction patterns. However, these approaches have high computational cost. In this paper, we introduce PtychoDV, a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction. PtychoDV comprises a vision transformer that generates an initial image from the set of raw measurements, taking into consideration their mutual correlations. This is followed by a deep unrolling network that refines the initial image using learnable convolutional priors and the ptychography measurement model. Experimental results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem, and significantly reduces computational cost compared to iterative methodologies, while maintaining competitive performance.

翻译：叠层衍射成像是一种通过移动局部照明探针相干照射样品，获取多个重叠快照的成像技术。该类成像数据的图像重建通常采用迭代算法，求解由实测衍射图谱导出的非线性相位场问题。然而，这类方法计算成本高昂。本文提出PtychoDV——一种新型基于深度模型的网络，专为高效、高质量叠层衍射图像重建而设计。PtychoDV包含一个视觉Transformer，该模块利用原始测量值之间的相互关联生成初始图像；随后通过深度展开网络，利用可学习卷积先验和叠层衍射测量模型对初始图像进行精化。模拟数据实验结果表明，PtychoDV在性能上优于现有深度学习方法，且与迭代算法相比，在保持竞争力性能的同时显著降低了计算成本。