  Digital in-line holography is commonly used to reconstruct 3D images from 2D
holograms for microscopic objects. One of the technical challenges that arise
in the signal processing stage is removing the twin image that is caused by the
phase-conjugate wavefront from the recorded holograms. Twin image removal is
typically formulated as a non-linear inverse problem due to the irreversible
scattering process when generating the hologram. Recently, end-to-end deep
learning-based methods have been utilized to reconstruct the object wavefront
(as a surrogate for the 3D structure of the object) directly from a single-shot
in-line digital hologram. However, massive data pairs are required to train
deep learning models for acceptable reconstruction precision. In contrast to
typical image processing problems, well-curated datasets for in-line digital
holography does not exist. Also, the trained model highly influenced by the
morphological properties of the object and hence can vary for different
applications. Therefore, data collection can be prohibitively cumbersome in
practice as a major hindrance to using deep learning for digital holography. In
this paper, we proposed a novel implementation of autoencoder-based deep
learning architecture for single-shot hologram reconstruction solely based on
the current sample without the need for massive datasets to train the model.
The simulations results demonstrate the superior performance of the proposed
method compared to the state of the art single-shot compressive digital in-line
hologram reconstruction method.
