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Title: Fast Computational Periscopy in Challenging Ambient Light Conditions through Optimized Preconditioning
Non-line-of-sight (NLOS) imaging is a rapidly advancing technology that provides asymmetric vision: seeing without being seen. Though limited in accuracy, resolution, and depth recovery compared to active methods, the capabilities of passive methods are especially surprising because they typically use only a single, inexpensive digital camera. One of the largest challenges in passive NLOS imaging is ambient background light, which limits the dynamic range of the measurement while carrying no useful information about the hidden part of the scene. In this work we propose a new reconstruction approach that uses an optimized linear transformation to balance the rejection of uninformative light with the retention of informative light, resulting in fast (video-rate) reconstructions of hidden scenes from photographs of a blank wall under high ambient light conditions.
Authors:
;
Award ID(s):
1955219
Publication Date:
NSF-PAR ID:
10267299
Journal Name:
Proc. IEEE Int. Conf. Computational Photography
Page Range or eLocation-ID:
1 to 9
Sponsoring Org:
National Science Foundation
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