4.7 Article

Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery

Journal

OPTICA
Volume 5, Issue 6, Pages 704-710

Publisher

Optica Publishing Group
DOI: 10.1364/OPTICA.5.000704

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Funding

  1. National Science Foundation (NSF)
  2. Howard Hughes Medical Institute (HHMI)
  3. Army Research Office (ARO)

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Holography encodes the three-dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), autofocusing and phase recovery are needed, which are in general cumbersome and time-consuming to perform digitally. Here we demonstrate a convolutional neural network (CNN)-based approach that simultaneously performs autofocusing and phase recovery to significantly extend the depth of field (DOF) and the reconstruction speed in holographic imaging. For this, a CNN is trained by using pairs of randomly defocused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase recovery and reconstruct an in-focus image of the sample over a significantly extended DOF. This deep-learning-based DOF extension method is non-iterative and significantly improves the algorithm time complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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