Deep DIH：通过深度学习对数字同轴全息进行统计推断重建
Deep DIH : Statistically Inferred Reconstruction of Digital In-Line Holography by Deep Learning
AI总结：
针对数字同轴全息孪生像去除中深度学习依赖大规模训练数据的问题，提出仅基于当前样本的自编码器架构实现单次重建，并在仿真中优于现有压缩重建方法。
摘要：
数字同轴全息通常用于根据显微物体的二维全息图重建三维图像。信号处理阶段出现的技术挑战之一，是从记录的全息图中去除由相位共轭波前引起的孪生像。由于生成全息图时散射过程不可逆，孪生像去除通常被表述为一个非线性逆问题。近来，基于端到端深度学习的方法已被用于直接从单次拍摄的同轴数字全息图重建物体波前（作为物体三维结构的替代表示）。然而，要训练深度学习模型以达到可接受的重建精度，需要大量数据对。与典型的图像处理问题不同，同轴数字全息领域并不存在经过精心整理的数据集。此外，训练后的模型会受到物体形态属性的高度影响，因此在不同应用之间可能发生变化。因此，数据收集在实践中可能极其繁琐，成为将深度学习用于数字全息的主要障碍。本文提出了一种基于自编码器的深度学习架构的新型实现，用于单次全息图重建；该方法仅依赖当前样本，无需使用大规模数据集来训练模型。仿真结果表明，与最先进的单次压缩数字同轴全息重建方法相比，所提方法具有更优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
