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首页> 外文会议>High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management >End-to-End Learning for Digital Hologram Reconstruction
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End-to-End Learning for Digital Hologram Reconstruction

机译：数字全息重建的端到端学习

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摘要

Digital holography is a well-known method to perform three-dimensional imaging by recording the light wavefront information originating from the object. Not only the intensity, but also the phase distribution of the wavefront can then be computed from the recorded hologram in the numerical reconstruction process. However, the reconstructions via the traditional methods suffer from various artifacts caused by twin-image, zero-order term, and noise from image sensors. Here we demonstrate that an end-to-end deep neural network (DNN) can learn to perform both intensity and phase recovery directly from an intensity-only hologram. We experimentally show that the artifacts can be effectively suppressed. Meanwhile, our network doesn't need any preprocessing for initialization, and is comparably fast to train and test, in comparison with the recently published learning-based method. In addition, we validate that the performance improvement can be achieved by introducing a prior on sparsity.
机译：数字全息术是通过记录源自物体的光波前信息来执行三维成像的公知方法。然后，在数值重建过程中不仅可以从记录的全息图计算出波前的强度，而且可以计算出波前的相位分布。但是，通过传统方法进行的重构会遭受由双图像，零阶项以及图像传感器产生的噪声造成的各种伪影。在这里，我们证明了端到端深度神经网络（DNN）可以直接从仅强度的全息图中学习直接执行强度和相位恢复。我们通过实验表明，可以有效地抑制伪像。同时，与最近发布的基于学习的方法相比，我们的网络不需要任何预处理即可进行初始化，并且训练和测试的速度相当快。此外，我们验证可以通过引入先验稀疏性来实现性能提升。

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