Name
Abstract | ||
|---|---|---|
Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We introduce a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum likelihood-based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and thus will be useful for rapid, low-power, and noise-tolerant active optical imaging. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025008 | ICIP |
Keywords | Field | DocType |
photon efficiency,high photon efficiency,low light-level imaging,time-of-flight,depth estimation,maximum likelihood estimation,reflectivity estimation,poisson noise,computational 3d imaging,computational imager,computational geometry,computational 3d,reflectivity,reflectivity imaging,maximum likelihood-based imaging methods,single-photon counting statistics,convex optimization,time of flight | Photon,Computer vision,Computer science,Pixel,Artificial intelligence,Reflectivity,Time of flight,Shot noise,Convex optimization,Detector,Active illumination | Conference |
ISSN | Citations | PageRank |
1522-4880 | 4 | 0.50 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongeek Shin | 1 | 21 | 3.47 |
| Ahmed Kirmani | 2 | 86 | 6.68 |
| Vivek K. Goyal | 3 | 2031 | 171.16 |
| Jeffrey H. Shapiro | 4 | 153 | 22.84 |