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Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data | | Rui Ding
; Juntian Ye
; Qifeng Gao
; Feihu Xu
; Yuping Duan
; | | Date: |
1 Jan 2023 | | Abstract: | Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional
hidden scenes from the data measured in the line-of-sight, which uses photon
time-of-flight information encoded in light after multiple diffuse reflections.
The under-sampled scanning data can facilitate fast imaging. However, the
resulting reconstruction problem becomes a serious ill-posed inverse problem,
the solution of which is of high possibility to be degraded due to noises and
distortions. In this paper, we propose two novel NLOS reconstruction models
based on curvature regularization, i.e., the object-domain curvature
regularization model and the dual (i.e., signal and object)-domain curvature
regularization model. Fast numerical optimization algorithms are developed
relying on the alternating direction method of multipliers (ADMM) with the
backtracking stepsize rule, which are further accelerated by GPU
implementation. We evaluate the proposed algorithms on both synthetic and real
datasets, which achieve state-of-the-art performance, especially in the
compressed sensing setting. All our codes and data are available at
this https URL | | Source: | arXiv, 2301.00406 | | Services: | Forum | Review | PDF | Favorites | |
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