A reconstruction method for ptychography based on residual dense network

Coherent diffraction imaging (CDI) is an important lens-free imaging method. As a variant of CDI, ptychography enables the imaging of objects with arbitrary lateral sizes. However, traditional phase retrieval methods are time-consuming for ptychographic imaging of large-size objects, e.g., integrated circuits (IC). Especially when ptychography is combined with computed tomography (CT) or computed laminography (CL), time consumption increases greatly.

In this work, we aim to propose a new deep learning-based approach to implement a quick and robust reconstruction of ptychography.

Inspired by the strong advantages of the residual dense network for computer vision tasks, we propose a dense residual two-branch network (RDenPtycho) based on the ptychography two-branch reconstruction architecture for the fast and robust reconstruction of ptychography. The network relies on the residual dense block to construct mappings from diffraction patterns to amplitudes and phases. In addition, we integrate the physical processes of ptychography into the training of the network to further improve the performance.

The proposed RDenPtycho is evaluated using the publicly available ptychography dataset from the Advanced Photon Source. The results show that the proposed method can faithfully and robustly recover the detailed information of the objects. Ablation experiments demonstrate the effectiveness of the components in the proposed method for performance enhancement.

The proposed method enables fast, accurate, and robust reconstruction of ptychography, and is of potential significance for 3D ptychography. The proposed method and experiments can resolve similar problems in other fields.