You are currently viewing the abstract.
View Full TextLog in to view the full text
AAAS login provides access to Science for AAAS members, and access to other journals in the Science family to users who have purchased individual subscriptions.
Register for free to read this article
As a service to the community, this article is available for free. Existing users log in.
More options
Download and print this article for your personal scholarly, research, and educational use.
Buy a single issue of Science for just $15 USD.
Computing an Image
Firing off a burst of laser pulses and detecting the back-reflected photons is a widely used method for constructing three-dimensional (3D) images of a scene. Kirmani et al. (p. 58, published online 29 November) describe an active imaging method in which pulsed laser light raster scans a scene and a single-photon detector is used to detect the first photon of the back-reflected laser light. Exploiting spatial correlations of photons scattered from different parts of the scene allows computation of a 3D image. Importantly, for biological applications, the technique allows the laser power to be reduced without sacrificing image quality.
Abstract
Imagers that use their own illumination can capture three-dimensional (3D) structure and reflectivity information. With photon-counting detectors, images can be acquired at extremely low photon fluxes. To suppress the Poisson noise inherent in low-flux operation, such imagers typically require hundreds of detected photons per pixel for accurate range and reflectivity determination. We introduce a low-flux imaging technique, called first-photon imaging, which is a computational imager that exploits spatial correlations found in real-world scenes and the physics of low-flux measurements. Our technique recovers 3D structure and reflectivity from the first detected photon at each pixel. We demonstrate simultaneous acquisition of sub–pulse duration range and 4-bit reflectivity information in the presence of high background noise. First-photon imaging may be of considerable value to both microscopy and remote sensing.
