  Data acquisition, image processing, and image quality are the long-lasting
issues for terahertz (THz) 3D reconstructed imaging. Existing methods are
primarily designed for 2D scenarios, given the challenges associated with
obtaining super-resolution (SR) data and the absence of an efficient SR 3D
reconstruction framework in conventional computed tomography (CT). Here, we
demonstrate BLIss, a new approach for THz SR 3D reconstruction with sparse 2D
data input. BLIss seamlessly integrates conventional CT techniques and
variational framework with the core of the adapted Euler-Elastica-based model.
The quantitative 3D image evaluation metrics, including the standard deviation
of Gaussian, mean curvatures, and the multi-scale structural similarity index
measure (MS-SSIM), validate the superior smoothness and fidelity achieved with
our variational framework approach compared with conventional THz CT modal.
Beyond its contributions to advancing THz SR 3D reconstruction, BLIss
demonstrates potential applicability in other imaging modalities, such as X-ray
and MRI. This suggests extensive impacts on the broader field of imaging
applications.
