Abstract
Reconstructing a scene's 3D structure and reflectivity accurately with an active imaging system operating in low-light-level conditions has wide-ranging applications, spanning biological imaging to remote sensing. Here we propose and experimentally demonstrate a depth and reflectivity imaging system with a single-photon camera that generates high-quality images from ∼1 detected signal photon per pixel. Previous achievements of similar photon efficiency have been with conventional raster-scanning data collection using single-pixel photon counters capable of ∼10-ps time tagging. In contrast, our camera's detector array requires highly parallelized time-to-digital conversions with photon time-tagging accuracy limited to ∼ns. Thus, we develop an array-specific algorithm that converts coarsely time-binned photon detections to highly accurate scene depth and reflectivity by exploiting both the transverse smoothness and longitudinal sparsity of natural scenes. By overcoming the coarse time resolution of the array, our framework uniquely achieves high photon efficiency in a relatively short acquisition time.
Highlights
Background lightProcessingsingle-photon avalanche diode (SPAD) camera Translator Field of view SyncPulse broadening Diffuser Pulsed laser b cScene of interest Frontal view Tilted viewPixelwise 3D and reflectivity reconstructionOur 3D and reflectivity reconstruction of all pixels in our case) so that their outputs could be ignored in the processing of the imaging data.We define Nz 1⁄4 dTr=De to be the total number of time bins in which the photon detections can be found, and let Ci,j,k be the observed number of photon counts in the kth time bin for pixel (i,j) after ns pulsed-illumination trials
We propose and demonstrate a photon-efficient 3D structure and reflectivity imaging technique that can deal with the aforementioned constraints that SPAD cameras impose
We have proposed and demonstrated a SPAD-camera imaging framework that generates highly accurate images of a scene’s 3D structure and reflectivity from B1 detected signal photon per pixel, despite the presence of extraneous detections at roughly the same rate from background light and dark counts
Summary
Background lightProcessingSPAD camera Translator Field of view SyncPulse broadening Diffuser Pulsed laser b cScene of interest Frontal view Tilted viewPixelwise 3D and reflectivity reconstructionOur 3D and reflectivity reconstruction of all pixels in our case) so that their outputs could be ignored in the processing of the imaging data.We define Nz 1⁄4 dTr=De to be the total number of time bins in which the photon detections can be found, and let Ci,j,k be the observed number of photon counts in the kth time bin for pixel (i,j) after ns pulsed-illumination trials. SPAD camera Translator Field of view Sync. Pulse broadening Diffuser Pulsed laser b c. Scene of interest Frontal view Tilted view. Our 3D and reflectivity reconstruction of all pixels in our case) so that their outputs could be ignored in the processing of the imaging data. We define Nz 1⁄4 dTr=De to be the total number of time bins in which the photon detections can be found, and let Ci,j,k be the observed number of photon counts in the kth time bin for pixel (i,j) after ns pulsed-illumination trials. By the theory of photon counting[28], we have that Ci,j,k’s statistical distribution is
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