Deep DIH: Single-Shot Digital In-Line Holography Reconstruction by Deep Learning

Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects from their 2D holograms. One of the technical challenges in the reconstruction stage is eliminating the twin image originating from the phase-conjugate wavefront. The twin image removal is typically formulated as a non-linear inverse problem since the scattering process involved in generating the hologram is irreversible. Conventional phase recovery methods rely on multiple holographic imaging at different distances from the object plane along with iterative algorithms. Recently, end-to-end deep learning (DL) methods are utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from the single-shot in-line digital hologram. However, massive data pairs are required to train the utilize DL model for an acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography do not exist. The trained models are also highly influenced by the objects' morphological properties, hence can vary from one application to another. Therefore, data collection can be prohibitively laborious and time-consuming, as a critical drawback of using DL methods for DH. In this article, we propose a novel DL method that takes advantages of the main characteristic of auto-encoders for blind single-shot hologram reconstruction solely based on the captured sample and without the need for a large dataset of samples with available ground truth to train the model. The simulation results demonstrate the superior performance of the proposed method compared to the state-of-the-art methods used for single-shot hologram reconstruction.