## Abstract

We demonstrate a high-throughput computation-efficient snapshot coherence tomographic imaging method by combining interferometric coding and compressive sampling. We first encode the depth distribution of a three-dimensional (3D) object into the spectrum of a light field, using the principle of optical coherence tomography (OCT), i.e., through a Michaelson interferometer, which generates an intermediate (x, y, \lambda) data-cube that encodes the raw (x, y, z) data of the object. We then sample the spectral data using a well-established compressive spectral imaging technique, called the coded aperture snapshot spectral imaging (CASSI), which yields a compressed 2D (x, y) measurement that captures the whole 3D tomographic information of the object. Finally, a developed iterative algorithm and end-to-end deep learning network are used for tomographic reconstruction from the single 2D measurement. Such integration of OCT and CASSI leads to a physically simple and computationally efficient system, allowing us to implement a large data size of more than 2000\times 2000 pixels in the transverse dimensions and up to 200 pixels (depth slices) in the axial dimension. Owning to the interferometry-based depth sensing mechanism, we achieve a high axial resolution of up to 13\,\mu m within an axial field of view of \text{1.6}\,mm. Video-rate visualization of dynamic 3D objects at micrometer scale are shown through several examples.

Original language | English (US) |
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Article number | 9463714 |

Pages (from-to) | 624-637 |

Number of pages | 14 |

Journal | IEEE Transactions on Computational Imaging |

Volume | 7 |

DOIs | |

State | Published - 2021 |

## All Science Journal Classification (ASJC) codes

- Signal Processing
- Computer Science Applications
- Computational Mathematics

## Keywords

- 3D imaging
- Compressive sensing
- coded aperture
- computational imaging
- deep learning
- interferometer