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.">

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