Open AccessThree-dimensional object visualization and detection in low light illumination using integral imaging
Author(s) -
Adam Markman,
Xin Shen,
Bahram Javidi
Publication year - 2017
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.42.003068
Subject(s) - integral imaging , visualization , optics , computer vision , artificial intelligence , detector , image sensor , computer science , object detection , ghost imaging , iterative reconstruction , physics , structured light , image (mathematics) , segmentation
Conventional two-dimensional (2D) imaging systems that operate in the visible spectrum may perform poorly in environments under low light illumination. In this work, we present the potential of passive three-dimensional (3D) integral imaging (II) to perform 3D imaging of a scene under low light conditions in the visible spectrum and without the need for a photon counting or cooled CCD camera. Using dedicated algorithms, we demonstrate that the reconstructed 3D integral image is naturally optimum in a maximum likelihood sense in low light levels and in the presence of detector noise enabling object visualization in the scene. The conventional 2D imaging fails due to the limited number of photons. Using 3D imaging, we demonstrate the potential for 3D detection of objects behind occlusion in a photon-starved scene. To the best of our knowledge, this is the first report of experimentally using II sensing under low illumination conditions for 3D visualization and 3D object detection in the presence of obscurations with a conventional image sensor.