Abstract

The recovery of obscured objects is an important goal in imaging that has been approached by exploiting coherence properties, ballistic photons, and penetrating wavelengths. In this paper, a robust reconstruction non-line-of-sight (NLOS) algorithm was proposed based on the Bayesian statistics, using the temporal, spatial, and intensity information of each signal. Compared with conventional back-projection methods, this algorithm is able to handle random errors in data and to image occluded objects with a higher quality. An adjustable compensation mechanism in our method is effective in dealing with the diversity of objects with regard to reflective characteristics. Our algorithm was demonstrated to be efficient with both simulated and experimental data. In addition, we discuss the advantages over existing methods and further improvement of the proposed algorithm.

© 2019 Optical Society of America

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